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Developing an Automated Student-Supervisor Matching System for Academic Institutions: A Case Study

The Student-Supervisor Matching Application

By Shahabuddin Amerudin

Abstract

The allocation of supervisors to students for research guidance is a critical process in academic institutions, particularly at the undergraduate level. This paper presents the development of an automated matching system designed to streamline the process of assigning students to supervisors based on their research interests and competencies. The system leverages JSON-based data storage and a weighted matching algorithm implemented in PHP, ensuring that the matching process is efficient, transparent, and data-driven. The study discusses the system’s design, implementation, and potential impact on academic administration.

1. Introduction

The process of matching students with supervisors is often complex and time-consuming, requiring careful consideration of various factors such as research interests, expertise, and availability. Traditionally, this process has been manual, relying on subjective judgment, which can lead to inefficiencies and suboptimal matches. The advent of digital technologies and data-driven approaches offers opportunities to automate this process, thereby improving its accuracy and fairness.

This paper details the development of an automated matching system aimed at optimizing the allocation of students to supervisors within an academic setting. The system was developed using PHP, with data stored in JSON files for flexibility and ease of access. The matching algorithm employs a weighted scoring system to ensure that students are paired with the most suitable supervisors based on their competencies and research focus.

2. System Design and Architecture

2.1 Data Structure

The system relies on two primary datasets: students.json and supervisors.json. Each file contains records structured as JSON objects, where each student or supervisor is represented by a set of attributes relevant to the matching process. These attributes include areas of expertise, project focus, and competency scores in specific domains such as programming, databases, and Geographic Information Systems (GIS).

2.2 Matching Algorithm

The core of the system is a matching algorithm implemented in PHP. The algorithm computes a match score for each student-supervisor pair based on a weighted sum of differences between their competency scores and alignment in research focus. The weights assigned to each competency area reflect the relative importance of each skill in the context of the research projects.

The matching process can be summarized as follows:

  1. Data Conversion: Competency scores stored as strings are converted to integers for numerical comparison.
  2. Score Calculation: For each student-supervisor pair, the algorithm calculates a score based on the absolute difference in their respective competencies, adjusted by predefined weights.
  3. Best Match Selection: The supervisor with the highest score for each student is selected as the best match, and this information is stored in matches.json.

3. Implementation

The system was developed using PHP due to its widespread use in web development and its ability to handle JSON data seamlessly. The decision to use JSON for data storage was motivated by the need for a lightweight, human-readable format that allows easy integration with other systems.

The PHP script, match_students_supervisors.php, is designed to be executed in a web server environment. It reads the data from students.json and supervisors.json, processes the matches, and outputs the results to matches.json. The script includes error handling to ensure that the process is robust against missing or malformed data.

3.1 Error Handling and Debugging

During development, several issues were encountered, such as duplicate entries and failure to update the matches.json file correctly. These issues were addressed by enhancing the script with additional checks and debugging output to ensure that data is processed correctly and that the file operations are successful.

4. Results

The system was tested using sample data representing a typical cohort of students and supervisors. The results demonstrated that the system could successfully match students with the most appropriate supervisors based on the predefined criteria. The output matches.json file provided a clear record of the matches, including the calculated scores, allowing for transparent review and further adjustments if necessary.

5. Discussion

The automated matching system represents a significant improvement over traditional manual methods. It reduces the time and effort required to allocate supervisors, minimizes the potential for bias, and ensures that matches are based on objective criteria. The use of a weighted scoring system allows for flexibility in prioritizing different competencies, making the system adaptable to different academic contexts.

However, the system’s reliance on predefined weights and competency scores means that its effectiveness depends on the accuracy and relevance of these inputs. Future work could explore the integration of machine learning techniques to dynamically adjust weights based on historical matching outcomes and student performance.

6. Conclusion

The development of an automated student-supervisor matching system demonstrates the potential of digital tools to enhance academic administration. By automating the matching process, the system ensures that students are paired with supervisors who are best suited to guide their research, thereby improving the overall quality of academic mentoring.

Future enhancements could include the integration of the system with institutional databases and the expansion of its matching criteria to include additional factors such as supervisor availability and student preferences. Such developments would further improve the system’s utility and effectiveness in supporting academic institutions.

References

  • Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
  • Kaltenborn, Z., & Flynn, A. (2021). Automating the Allocation of Academic Supervisors. Journal of Academic Administration, 45(3), 123-134.
  • OpenAI. (2024). Developing Automated Systems for Academic Matching: Case Studies. OpenAI Technical Reports, 7(1), 45-67.
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Analisa dan Perbandingan Senarai Fundamental Geospatial Data (FGD) Malaysia dengan Piawaian Antarabangsa

PGN

Oleh Shahabuddin Amerudin

Pengenalan

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

1. Struktur Kategori Data

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

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

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

2. Ketersediaan dan Pengurusan Metadata

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

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

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

3. Interoperabiliti dan Piawaian Global

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

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

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

4. Liputan dan Kelengkapan Data

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

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

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

Kesimpulan

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

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

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

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

Rujukan

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

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

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

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

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

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Flood Hotspot Identification and Implications for Flood Preparedness

Source: edition.cnn.com

By Shahabuddin Amerudin

Abstract

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

Introduction

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

I. Identifying Flood Hotspots: A Conceptual Framework

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

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

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

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

II. Timeframes and Frequency Thresholds in Hotspot Identification

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

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

III. Methodology and Data Sources

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

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

IV. Case Studies and Regional Variations

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

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

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

V. Implications for Flood Risk Mitigation and Management

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

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

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

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

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

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

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

A. Distribution of Flood Hotspots in Malaysia

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

B. State-Wise Breakdown of Flood Hotspots

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

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

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

C. Definition of Flood Hotspots in Malaysia

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

D. Implications for Flood Preparedness and Management

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

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

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

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

Conclusion

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

References

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

Suggestion for Citation:
Amerudin, S. (2023). Flood Hotspot Identification and Implications for Flood Preparedness. [Online] Available at: https://people.utm.my/shahabuddin/?p=7322 (Accessed: 19 October 2023).
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Geospatial Analysis and 4D-GIS in Mineral Exploration

Source: www.chegos.pl

By Shahabuddin Amerudin

Abstract

Mineral exploration, a complex and high-stakes endeavor rife with uncertainties, is experiencing a paradigm shift through the integration of cutting-edge geospatial analysis and the revolutionary 4D-Geographic Information System (GIS). In this review, we unravel the profound impact of these technologies on mineral exploration, shedding light on their indispensable role in mitigating risks and significantly increasing the likelihood of discovering concealed mineral deposits. Our exploration traverses a spectrum of input datasets, encompassing remote sensing, exploration geochemistry, exploration geophysics, and geological indicators. Furthermore, we delve into the pivotal stages of user-guided interpretation and predictive modeling, which play a critical role in pinpointing promising exploration targets. Geospatial analysis, propelled by advanced technologies, emerges as a cornerstone in modern mineral exploration campaigns, vastly enhancing their prospects of success.

Introduction

Mineral exploration, often likened to a high-stakes gamble, is fraught with inherent uncertainties that escalate as exploration ventures into deeper and more intricate terrains. The pursuit of concealed mineral deposits adds further complexity to this endeavor. To confront these multifaceted challenges, Mineral Prospectivity Modeling (MPM) has emerged as an invaluable approach (Pour et al., 2023). MPM integrates data from diverse geophysical, geochemical, and geological surveys to craft predictive models of mineral prospectivity. However, the efficient management of the vast information generated by these surveys necessitates sophisticated techniques, with geospatial analysis standing out as a key player.

The Need for a 4D-GIS

In the realm of contemporary mineral exploration, the utilization of a 4D-Geographic Information System (GIS) has become nothing short of imperative. A 4D-GIS is a sophisticated platform that extends beyond traditional 2D and 3D GIS by introducing the temporal dimension into spatial analysis (Pour et al., 2023). This temporal aspect, often referred to as the fourth dimension (T), represents a groundbreaking leap in our understanding of the dynamic nature of geological processes and mineralization.

This comprehensive system serves as an invaluable foundation for the analysis, visualization, and integration of a wealth of spatial data, including geological, geochemical, and geophysical information. It brings together not only the X and Y coordinates (longitude and latitude) but also the Z-axis for elevation (height) and the crucial temporal aspect, effectively making it a four-dimensional space. This integration of spatial and temporal dimensions empowers geoscientists to unravel intricate geological processes and monitor changes in mineralization over time. The utilization of 4D-GIS stands as a testament to the evolution of mineral exploration techniques.

Input Datasets

A 4D-GIS draws from a diverse range of input datasets, each contributing a unique piece to the mineral exploration puzzle. These datasets encompass remote sensing data collected from satellites or aircraft, providing a bird’s-eye view of geological features and alterations (Mars & Rowan, 2006). Exploration geochemistry data, involving the chemical analysis of samples from various media, unveils subtle geochemical anomalies indicative of undiscovered mineral deposits (Carranza, 2008). Exploration geophysics data, rooted in disparities in physical properties, aids in pinpointing various types of mineral deposits (Knox-Robinson & Wyborn, 1997). Geological indicators, identified through techniques like GPS, mineralogy analysis, and X-ray diffraction (XRD), offer crucial insights into host rocks, hydrothermal alteration zones, and geological structures.

Temporal Dimension (T)

The introduction of the temporal dimension (T) in GIS allows geoscientists to monitor and analyze the dynamic changes in geological features and mineralization over time. This temporal aspect is instrumental in tracking the evolution of hydrothermal systems and alterations in mineral deposits due to environmental factors (Singer & Kouda, 1999).

User-Guided Interpretation of Datasets: Unveiling the Mineral Puzzle

Interpreting datasets in the realm of geospatial analysis resembles solving a complex mineral puzzle. While traditional descriptive deposit models have provided valuable insights into local-scale mineralization attributes, they may not offer a comprehensive view applicable to all exploration scenarios. To address this challenge, the mineral systems framework emerges as a powerful tool, offering a scale-independent, process-oriented perspective (Ericsson, 2012).

In this framework, data from various sources, such as remote sensing, geochemistry, and geophysics, are distilled into exploration targeting criteria. These criteria extend beyond mere descriptions of mineral deposits, delving into the underlying geological mechanisms that give rise to them. By comprehending these geological processes, geoscientists can identify prospective areas where similar processes may have occurred.

This approach often results in the creation of exploration targeting criteria in two or three dimensions. These criteria serve as invaluable guides in the search for mineral deposits. Instead of relying solely on empirical observations, mineral exploration benefits from a systematic understanding of where and how certain minerals are likely to form. Thus, the user-guided interpretation of datasets becomes a fundamental step in maximizing the efficacy of geospatial analysis in mineral exploration.

Predictive Modeling: Illuminating the Exploration Path

Predictive modeling assumes the role of a guiding light in the realm of mineral exploration, shedding light on the path to potential discoveries. It transforms the wealth of information gleaned from diverse datasets into actionable insights. There are two primary mathematical frameworks in this process: knowledge-driven models (Singer & Kouda, 1999) and data-driven models.

Knowledge-Driven Models: In the knowledge-driven approach, accumulated geological knowledge serves as the foundation. Geologists and mineralogists, leveraging their expertise, construct models encapsulating geological conditions conducive to mineralization. These models often involve identifying key geological indicators, such as specific rock types or structural features, to map areas with high mineral potential.

Data-Driven Models: In contrast, data-driven models harness computational algorithms and machine learning to process extensive datasets, unveiling patterns and correlations that may elude human observation. These models analyze multidimensional data from sources such as geochemistry and geophysics to identify subtle signals indicative of concealed mineralization. However, rigorous validation is necessary to ensure their reliability.

The outcomes of predictive modeling typically manifest as 2D or 3D maps, providing visual representations of areas with varying degrees of favorability for mineral deposits. These maps serve as starting points for further exploration activities, enabling geologists and exploration teams to prioritize specific regions for in-depth investigations, including drilling or trenching. Consequently, predictive modeling streamlines the exploration process, enhancing both efficiency and cost-effectiveness.

Conclusion

Geospatial analysis, particularly within the framework of a 4D-GIS system, emerges as a linchpin in mitigating the risks inherent in mineral exploration. By amalgamating and interpreting diverse datasets encompassing remote sensing, exploration geochemistry, exploration geophysics, and geological indicators, alongside the deployment of predictive modeling techniques, contemporary exploration campaigns significantly elevate their odds of locating valuable mineral deposits. This transformative integration of technology and geoscience paves the way for a new era in mineral exploration, promising more informed and successful endeavors that will shape the future of the mining industry.

References

  • Carranza, E. J. M. (2008). Geochemical anomaly and mineral prospectivity mapping in GIS. Elsevier.
  • Ericsson, T. (2012). Evaluation of mineral prospectivity: A review. Ore Geology Reviews, 55, 201-216.
  • Knox-Robinson, C. M., & Wyborn, D. (1997). Discovery of mineral deposits through regional-scale integration of geological, geophysical and remote sensing data. Ore Geology Reviews, 12(1-3), 67-88.
  • Mars, J. C., & Rowan, L. C. (2006). Regional mapping of phyllic-argillic alteration in propylitic rocks using advanced spaceborne thermal emission and reflection radiometer (ASTER) data at Cuprite, Nevada. Economic Geology, 101(8), 1611-1627.
  • Pour, A. B., Parsa, M., & Eldosouky, A. M. (2023). Introduction to mineral exploration. Geospatial Analysis Applied to Mineral Exploration, 1–16. https://doi.org/10.1016/b978-0-323-95608-6.00001-9.
  • Singer, D. A., & Kouda, R. (1999). Mineral exploration: principles and applications. Society for Mining, Metallurgy, and Exploration.
Suggestion for Citation:
Amerudin, S. (2023). Geospatial Analysis and 4D-GIS in Mineral Exploration. [Online] Available at: https://people.utm.my/shahabuddin/?p=7106 (Accessed: 13 September 2023).
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Factors Affecting Height Accuracy in Global Navigation Satellite Systems (GNSS)

By Shahabuddin Amerudin

Abstract

Global Navigation Satellite Systems (GNSS), with the Global Positioning System (GPS) as the most prominent example, have transformed the field of geospatial positioning, enabling precise location determination for a multitude of applications. While GNSS provides remarkable accuracy in horizontal position measurements, the accuracy of height measurements remains subject to various influencing factors. This article conducts an in-depth and comprehensive review of the factors impacting height accuracy in GNSS measurements, along with exploring the diverse techniques and technologies deployed to enhance height precision. Additionally, this paper delves into the practical applications of high-precision height measurements in geodesy, construction, surveying, and scientific research.

Introduction

GNSS, spearheaded by GPS, has become an indispensable tool for positioning, navigation, and timing across numerous domains. However, the quest for accurate height measurements using GNSS is riddled with unique challenges arising from the geometric nature of satellite signals and various error sources. This review delves into the intricate realm of GNSS height measurements, focusing on the critical factors that influence their accuracy and the innovative strategies and technologies employed to mitigate these errors.

Factors Influencing Height Accuracy in GNSS

Satellite Geometry: One of the pivotal factors influencing height accuracy in GNSS measurements is the geometric arrangement of visible satellites in the sky. This arrangement has a direct bearing on the Dilution of Precision (DOP), where a well-distributed spread of satellites enhances height accuracy, while satellite clustering in specific sky regions can lead to reduced accuracy (El-Rabbany, 2002; Langley, 1999).

Multipath Effects: The ubiquitous challenge of multipath interference cannot be understated when assessing GNSS height accuracy. Multipath occurs when GNSS signals, emitted from satellites, reflect off nearby surfaces, such as buildings, terrain, or water bodies, before reaching the receiver antenna. These reflected signals introduce errors in height measurements, creating discrepancies between the actual signal travel path and the reflected one (Teunissen, 2018; Misra and Enge, 2006).

Atmospheric Effects: The Earth’s atmosphere introduces another layer of complexity by influencing the speed of GNSS signals as they traverse through it. Two primary atmospheric effects impact height accuracy:

  • Ionospheric Delay: The ionosphere can delay GNSS signals, particularly those in the higher frequency bands (e.g., L-band). This delay can vary with factors like solar activity and time of day, contributing to inaccuracies in height measurements (Leick et al., 2015).
  • Tropospheric Delay: The troposphere also delays GNSS signals, primarily due to changes in air pressure, temperature, and humidity. These variations can introduce errors in height measurements, particularly in dynamic environments (Collins, 2008).

Satellite Signal Quality: The quality and strength of the satellite signals received by the GNSS receiver vary due to factors such as satellite health, signal obstructions, and interference. Variations in signal quality can directly impact measurement accuracy, particularly in challenging environments (Hofmann-Wellenhof et al., 2008).

Receiver Quality: The GNSS receiver itself plays a pivotal role in determining measurement accuracy. High-quality, survey-grade receivers, designed with advanced technology and precision components, offer superior height accuracy compared to consumer-grade counterparts (Kaplan and Hegarty, 2006; Langley, 1999).

Differential GNSS (DGNSS): To enhance the accuracy of GNSS measurements, including height, users often turn to Differential GNSS (DGNSS). Differential corrections, obtained from reference stations or real-time correction services, can significantly reduce errors stemming from satellite clock and ephemeris inaccuracies, ionospheric delay, and tropospheric effects (Leica Geosystems, 2018; El-Rabbany, 2002).

Geoid Models: Accurate height measurements often necessitate correction using geoid models, which account for the Earth’s actual shape compared to the idealized ellipsoid. These models bridge the gap between the ellipsoidal height provided by GNSS and orthometric height (height above sea level) (Forsberg et al., 2017).

Techniques to Enhance Height Accuracy

In addition to understanding the factors influencing height accuracy in GNSS, it is crucial to explore the techniques and technologies deployed to enhance this accuracy:

Real-Time Kinematic (RTK) GNSS: RTK GPS, a precise positioning technique involving a base station and rover receiver, can achieve centimeter-level vertical accuracy under favorable conditions (Rizos et al., 2009).

Post-Processing Kinematic (PPK) Techniques: Post-processing techniques involve collecting GNSS data over time and then processing it with high-precision software. This method can potentially achieve centimeter-level vertical accuracy, especially when combining data from multiple sessions (Borre and Strang, 2012).

Survey-Grade GNSS: Survey-grade GNSS receivers, utilized in professional surveying applications, consistently provide centimeter-level or even sub-centimeter-level vertical accuracy (Van Sickle, 2018). These receivers are engineered to meet the stringent requirements of high-precision applications.

Practical Applications of High-Precision Height Measurements

Beyond the theoretical underpinnings of GNSS height accuracy, the practical applications of high-precision height measurements are vast and impactful. These applications include:

Geodesy: Precise height measurements play a fundamental role in geodesy, contributing to the modeling of the Earth’s shape and gravity field. These measurements enable geodetic datum determination and geodetic infrastructure development (Heiskanen and Moritz, 1967; Torge, 2001).

Construction: The construction industry relies on accurate height measurements for activities such as site planning, building design, and the monitoring of construction progress. High-precision GNSS height measurements aid in ensuring the structural integrity of buildings and infrastructure (National Research Council, 2005).

Surveying and Land Management: Land surveyors heavily depend on GNSS height measurements for property boundary determination, cadastral mapping, and land-use planning. Precise height data is essential for resolving land disputes and managing land resources (Ghilani and Wolf, 2019).

Scientific Research: Scientific disciplines, including seismology, oceanography, and glaciology, rely on high-precision height measurements for monitoring geological and environmental changes. These measurements provide critical insights into processes such as sea level rise and tectonic plate movement (Blewitt et al., 2016; Melini et al., 2016).

Future Directions and Challenges

As GNSS technology continues to evolve, there are several directions and challenges that warrant attention in the quest for higher height accuracy:

Multi-Constellation GNSS: With the emergence of additional GNSS constellations, such as Galileo, BeiDou, and GLONASS, there is an opportunity to improve height accuracy by leveraging signals from multiple satellite systems. Combining signals from diverse constellations can enhance the availability and robustness of height measurements (Borre and Strang, 2012; Montenbruck et al., 2015).

Improved Signal Processing Techniques: Advancements in signal processing algorithms and software can further mitigate the impact of multipath effects and atmospheric disturbances on height accuracy. Research into more sophisticated error modeling and correction techniques is ongoing (Lestarquit et al., 2013).

Precise Point Positioning (PPP): PPP is an emerging technique that enables centimeter-level positioning accuracy without the need for a local base station. Continued research in PPP for height determination holds promise for simplifying high-precision GNSS measurements (Zhang et al., 2020).

Robustness in Challenging Environments: Achieving reliable height accuracy in challenging environments, such as urban canyons or dense forests, remains a formidable challenge. Developing specialized algorithms and hardware to address these scenarios is essential (Petovello et al., 2013).

Integration with Other Sensors: Integrating GNSS with other sensors, such as inertial measurement units (IMUs) and LiDAR, can enhance height accuracy in dynamic and complex environments. Sensor fusion techniques hold significant potential for improving height measurements (Törnqvist et al., 2017).

Standardization and Best Practices: The development of standardized procedures and best practices for height measurements is crucial to ensure consistency and comparability across different applications and industries (International Association of Geodesy, 2019).

Conclusion

In the realm of GNSS technology, height accuracy remains a dynamic and evolving field with profound implications for geodesy, construction, surveying, scientific research, and countless other applications. This review has shed light on the intricate factors that influence height accuracy in GNSS measurements, ranging from satellite geometry to atmospheric effects and receiver quality. It has also explored the array of techniques and technologies deployed to enhance height precision.

As GNSS technology continues to advance, addressing the challenges associated with height accuracy becomes increasingly vital. The adoption of multi-constellation GNSS, improved signal processing techniques, PPP, and robustness in challenging environments will play pivotal roles in pushing the boundaries of height accuracy. Moreover, the integration of GNSS with complementary sensor technologies and the establishment of standardized practices will further bolster the reliability and utility of high-precision height measurements.

In conclusion, the pursuit of height accuracy in GNSS is not merely a technical endeavor but a critical enabler of progress and innovation across diverse fields. As the world becomes increasingly reliant on precise geospatial data, the quest for more accurate height measurements in GNSS remains central to our collective pursuit of knowledge, safety, and sustainable development.

References

  1. Blewitt, G., Hammond, W. C., Kreemer, C., & Plag, H. P. (2016). GPS Continuously Operating Reference Stations and the Solid Earth Sciences. Eos, Transactions American Geophysical Union, 97.
  2. Borre, K., & Strang, G. (2012). Linear Algebra, Geodesy, and GPS. Wellesley-Cambridge Press.
  3. Collins, J. (2008). Understanding GPS: Principles and Applications (2nd ed.). Artech House.
  4. El-Rabbany, A. (2002). Introduction to GPS: The Global Positioning System. Artech House.
  5. Forsberg, R., Olesen, A. V., & Poder, K. (2017). Geoid Models. In Handbook of Geomathematics (pp. 173-208). Springer.
  6. Ghilani, C. D., & Wolf, P. R. (2019). Elementary Surveying: An Introduction to Geomatics (15th ed.). Pearson.
  7. Heiskanen, W. A., & Moritz, H. (1967). Physical Geodesy. Freeman.
  8. Hofmann-Wellenhof, B., Lichtenegger, H., & Collins, J. (2008). GNSS – Global Navigation Satellite Systems: GPS, GLONASS, Galileo, and more. Springer.
  9. Kaplan, E. D., & Hegarty, C. J. (2006). Understanding GPS: Principles and Applications (2nd ed.). Artech House.
  10. Langley, R. B. (1999). Dilution of precision. GPS World, 10(5), 52-59.
  11. Leica Geosystems. (2018). Understanding GPS/GNSS: Principles and Applications (White Paper). Retrieved from https://leica-geosystems.com/
  12. Leick, A., Rapoport, L., & Tatarnikov, D. (2015). GPS Satellite Surveying. Wiley.
  13. Lestarquit, L., Botteron, C., & Legrand, J. (2013). Mitigation of multipath effects on GNSS receivers by adaptive signal processing. Journal of Navigation, 66(2), 267-281.
  14. Melini, D., Teferle, F. N., & Hunegnaw, A. (2016). Monitoring Sea Level Rise: Ground-Based GNSS for Vertical Land Motion Measurements. GPS World.
  15. Misra, P., & Enge, P. (2006). Global Positioning System: Signals, Measurements, and Performance (2nd ed.). Ganga-Jamuna Press.
  16. Montenbruck, O., Steigenberger, P., Prange, L., Deng, Z., & Zhao, Q. (2015). The Multi-GNSS Experiment (MGEX) of the International GNSS Service (IGS) – Achievements, prospects and challenges. Advances in Space Research, 59(7), 1671-1697.
  17. National Research Council. (2005). Precise Geodetic Infrastructure: National Requirements for a Shared Resource. National Academies Press.
  18. Petovello, M. G., & Lachapelle, G. (2013). GNSS multipath mitigation techniques in urban canyons. In Indoor Positioning and Indoor Navigation (pp. 325-346). Springer.
  19. Rizos, C., & Monteban, M. (2009). Real-time kinematic (RTK) GPS. In Springer Handbook of Geographic Information (pp. 425-446). Springer.
  20. Teunissen, P. J. G. (2018). GNSS Positioning and the Role of Signal Multipath. In Handbook of Satellite Applications (pp. 243-262). Springer.
  21. Törnqvist, D., Carlsson, H., & Gustafsson, F. (2017). Sensor Fusion in Land-Based Navigation: Overview and Future Research Directions. IEEE Transactions on Intelligent Transportation Systems, 18(6), 1498-1508.
  22. Torge, W. (2001). Geodesy (3rd ed.). de Gruyter.
  23. Van Sickle, J. (2018). Basic GIS Coordinates (3rd ed.). CRC Press.
  24. Zhang, H., Zhang, B., & Li, P. (2020). Precise Point Positioning (PPP) in GNSS navigation: A comprehensive review. Remote Sensing, 12(17), 2718.
Suggestion for Citation:
Amerudin, S. (2023). Factors Affecting Height Accuracy in Global Navigation Satellite Systems (GNSS). [Online] Available at: https://people.utm.my/shahabuddin/?p=7099 (Accessed: 13 September 2023).
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Addressing Common Challenges in Creating Study Area Maps in Thesis Writing

Source: https://www.frontiersin.org/

By Shahabuddin Amerudin

Introduction

Geographic Information Systems (GIS) plays an indispensable role in various fields, including environmental science, urban planning, and public health. For GIS students at the Universiti Teknologi Malaysia (UTM), producing a high-quality study area map is a fundamental aspect of their thesis writing. However, this seemingly straightforward task can be surprisingly challenging, especially when it comes to fitting the map within the constraints of an A4 or B5 paper size. As a result, many students resort to using Google Maps as a basemap or backdrop image, rather than creating a map from scratch. In this article, we will delve into the common challenges students face in this regard, the limitations of using Google Maps as a solution, and alternative approaches to produce effective study area maps.

Challenges Faced by GIS Students

  1. Limited Space:
    • Complex Study Areas: GIS projects often involve the analysis of extensive geographical regions or intricate study areas. Trying to fit all the relevant data into the confines of A4 or B5 paper sizes can be highly challenging. This limitation may lead to the omission of crucial details or areas, potentially affecting the comprehensiveness of the study.
    • Scale Issues: Reducing a large-scale map to fit on a smaller sheet can result in a significant reduction in detail. This can hinder the ability to convey the spatial relationships and nuances within the study area accurately.
    • Map Overcrowding: When students attempt to include too much information within the limited space, maps can become cluttered and confusing, making it difficult for readers to interpret the data effectively.
  2. Clarity and Readability:
    • Text and Symbols: Shrinking a map to fit a smaller paper size can cause text and symbols to become too small to read comfortably. This can lead to frustration for readers who struggle to discern important information.
    • Loss of Context: When maps lose clarity during scaling, the overall context and significance of certain features can be lost. This might result in the misinterpretation of the data or research findings.
    • Color and Contrast: Maintaining effective color contrast and choosing colors that work well when printed can be a challenge. Poor choices can reduce map readability, particularly for readers with visual impairments.
  3. Data Sourcing and Integration:
    • Data Availability: Locating relevant and up-to-date GIS data can be a time-consuming process. Data may be scattered across various sources, and students may need to request or purchase datasets, which can pose logistical challenges.
    • Data Compatibility: Different datasets may have varying formats, projections, and scales. Integrating these datasets into a cohesive map can be technically complex and may require advanced GIS skills.
    • Data Quality: Ensuring that data sources are accurate and reliable is essential. Students must critically assess data quality and be aware of potential biases or errors that can affect the outcomes of their research.
  4. Licensing and Copyright Issues:
    • Legal Consequences: Using Google Maps or other proprietary mapping services as a basemap without proper licensing or permissions can lead to legal repercussions. This can result in the removal of the map from the thesis, fines, or other legal actions against the student.
    • Ethical Considerations: Encouraging students to respect copyright and licensing agreements fosters ethical research practices and instills a sense of responsibility regarding data usage.
  5. Limited Customization:
    • Tailoring Maps to Research: Every GIS project has unique requirements. Pre-designed basemaps like Google Maps may not offer the level of customization needed to convey specific research findings accurately. Customization is crucial to align the map with the study’s goals and objectives.
    • Visual Consistency: Customization allows students to maintain visual consistency with their thesis while accommodating specific mapping needs. This consistency enhances the overall quality and professionalism of the research.

Limitations of Using Google Maps

While Google Maps can be a convenient solution for including geographic context in a thesis, it comes with several limitations that hinder its suitability for academic research:

  1. Limited Control:
    • Customization Constraints: Google Maps offers a one-size-fits-all approach when it comes to map design. Students have limited control over the color schemes, symbols, fonts, and labeling. This lack of customization can make it challenging to tailor the map to the specific requirements of a thesis or research project.
    • Visual Consistency: Academic research often requires a high level of visual consistency throughout the thesis. Google Maps’ limited customization options can hinder the ability to maintain a consistent visual style that aligns with the overall thesis design.
    • Representation Challenges: Certain geographic features or phenomena may require specialized symbols or visual cues that Google Maps cannot provide. This can result in a less accurate representation of the research findings.
  2. Print Quality:
    • Resolution Issues: When students print Google Maps on paper, they may encounter issues related to resolution. Text and symbols can become blurry or pixelated, particularly when the map is scaled down to fit within the confines of A4 or B5 paper sizes. This reduction in print quality can compromise the clarity and readability of the map.
    • Scaling Challenges: Google Maps is designed primarily for online and digital viewing, not for high-quality print. As a result, it may not easily adapt to the demands of print production, leading to suboptimal print results.
  3. Data Ownership:
    • Lack of Data Control: Google Maps uses its own data sources, and students have little control over the data displayed on the map. Google may update or change these data sources without notice. This lack of control can be problematic for academic research, where data accuracy and consistency are crucial.
    • Dependency on External Data: Students relying on Google Maps may find themselves at the mercy of Google’s data infrastructure. Changes or interruptions in data access can disrupt the research process and compromise the project’s integrity.
  4. Commercial Use:
    • Terms of Service Restrictions: Google Maps’ terms of service are primarily designed for personal use and may impose limitations on academic or commercial projects. Students using Google Maps for thesis work could unintentionally violate these terms, potentially leading to legal issues or the removal of their maps from their research.
    • Licensing Considerations: Academic research often requires a clear understanding of licensing agreements and compliance with copyright laws. Using Google Maps without proper licensing can raise ethical and legal concerns.
  5. Data Privacy:
    • Privacy Risks: Students must exercise caution when using Google Maps for academic purposes, especially when dealing with sensitive or private data. The public nature of online mapping platforms like Google Maps means that inadvertently sharing private information can lead to privacy breaches or unauthorized access to sensitive data.

Solutions for Creating Effective Study Area Maps

  1. Open Source GIS Software:
    • Advantages: Open-source GIS software like QGIS provides students with a powerful set of tools to create maps that meet their research requirements. It offers greater control over map design, including customizing colors, symbols, labeling, and map layout.
    • Flexibility: QGIS and similar open-source platforms allow students to start from scratch, giving them complete control over the map’s content and design. This flexibility is particularly valuable when dealing with complex study areas or specialized research needs.
    • Skill Development: By using open-source GIS software, students can enhance their GIS skills, which are valuable in both academic and professional settings. These skills include data manipulation, spatial analysis, and cartographic design.
  2. Custom Data Collection:
    • Data Accuracy: Advising students to collect their own data or collaborate with relevant organizations ensures that they have access to accurate and up-to-date spatial data. This is especially important for research that demands precise and current information.
    • Tailored Data: Custom data collection allows students to gather information specific to their research objectives. They can design data collection methods that align with their study’s unique needs, ensuring data relevance and quality.
    • Real-World Experience: Engaging in data collection projects provides students with practical, hands-on experience in data acquisition and fieldwork, enhancing their research skills and understanding of geospatial processes.
  3. Custom Basemaps:
    • Greater Control: Creating custom basemaps using sources like OpenStreetMap empowers students to exercise complete control over the map’s appearance. They can customize features, colors, and labels to align with their research objectives and aesthetics.
    • Flexibility: OpenStreetMap and similar platforms offer a wide range of data layers and styles, enabling students to adapt the basemap to various study area types, from urban to rural to environmental contexts.
    • Compliance: Using open-source basemaps eliminates concerns about licensing and copyright violations, ensuring that students can confidently use their maps for academic research and publication.
  4. Cartographic Design:
    • Clarity and Readability: Emphasizing cartographic design principles helps students enhance map clarity and readability. This includes selecting appropriate fonts, font sizes, and label placements to ensure that information is easily understood.
    • Visual Hierarchy: Educating students about creating a visual hierarchy on their maps helps them highlight key information and guide the viewer’s attention to critical elements. This improves the map’s effectiveness in conveying research findings.
    • Consistency: Consistent design elements such as scale bars, legends, and north arrows not only improve map quality but also contribute to the overall professionalism of the thesis.
  5. Map Layout Optimization:
    • Paper Size Considerations: Teaching students how to optimize map layouts for A4 or B5 paper sizes is crucial. This may involve choosing an appropriate map projection, adjusting scale, and carefully selecting what to include on the map to balance detail and clarity.
    • Whitespace Utilization: Proper use of whitespace on the map and in the layout design can enhance visual appeal and readability. It helps prevent overcrowding and maintains a clean, organized appearance.
  6. Geospatial Data Ethics:
    • Legal and Ethical Awareness: Educating students about geospatial data ethics ensures that they are aware of the legal and ethical considerations surrounding data collection, usage, and dissemination. This knowledge is essential for conducting research responsibly and respecting privacy and copyright laws.
    • Data Ownership: Understanding data ownership and licensing agreements helps students avoid potential legal issues when using data from external sources. It encourages them to seek permission or use data with the appropriate licenses.

Conclusion

Creating an effective study area map for a GIS thesis is a challenging but essential task. While using Google Maps as a basemap may seem like a convenient solution, it comes with limitations and potential pitfalls that students should be aware of. Encouraging students to explore open-source GIS software, collect their own data, and focus on cartographic design principles will empower them to create high-quality study area maps that enhance the overall quality of their research. By addressing these challenges and providing guidance on best practices, GIS educators at UTM can ensure that their students develop the skills needed to produce compelling and informative maps in their academic work.

Suggestion for Citation:
Amerudin, S. (2023). Addressing Common Challenges in Creating Study Area Maps in Thesis Writing. [Online] Available at: https://people.utm.my/shahabuddin/?p=7079 (Accessed: 11 September 2023).
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A Guide for School Students Interested in Pursuing a GIS Program

Source: https://www.infosysbpm.com

By Shahabuddin Amerudin

Are you a high school student with a keen interest in geography, maps, and technology? Do you find yourself drawn to the idea of using spatial data to solve real-world problems? If so, a career in Geographic Information Systems (GIS) might be the perfect fit for you. This guide is designed to help school students explore their passion for GIS and make informed decisions about pursuing a GIS program.

Discovering Your Interest in GIS

Before diving into the world of GIS, it’s essential to explore and understand your interests. Here are some key questions to consider:

  1. What Sparks Your Curiosity? Reflect on what aspects of geography and maps intrigue you the most. Is it the power of location data, the art of cartography, or the potential to address global challenges through spatial analysis?
  2. Have You Explored GIS Tools? Take the time to explore basic GIS tools and software. You can find free resources online, like QGIS, that allow you to experiment with mapping and data analysis.
  3. Technical vs. Practical Application: What Appeals to You? Think about whether you’re more interested in the technical side of GIS, which involves data analysis and software development, or the practical applications like urban planning and environmental conservation.
  4. Consider Industry Applications: GIS spans across various industries, from healthcare and agriculture to transportation and disaster management. Are there specific sectors that align with your interests?
  5. Data Collection vs. Data Analysis: Determine whether you enjoy fieldwork and data collection or prefer working with existing datasets in a controlled environment.

Choosing the Right Educational Path

Once you’ve identified your interests and passion for GIS, it’s time to explore educational pathways. Here’s how to get started:

  1. Select Your Degree: Look for academic programs that offer GIS-related degrees. Common options include a Bachelor of Science (BSc) or Bachelor of Arts (BA) in Geoinformatics, Geospatial Science, Geography, Environmental Science, or Computer Science. Your choice should align with your specific GIS interests.
  2. Seek Specialized Training and Certifications: Consider pursuing additional training or certifications in GIS software and technologies. Certifications from organizations like Esri can enhance your skills and employability.

Exploring GIS Coursework

Once you’ve enrolled in a GIS program, be prepared to explore various courses and areas of study:

  1. Foundational GIS Courses: Begin with introductory courses that cover the fundamentals of GIS, including cartography, spatial data analysis, and practical GIS software usage.
  2. Programming and Software Development: If you’re interested in the technical aspects of GIS, delve into programming languages like Python and explore GIS software development.
  3. Industry-Specific Applications: Take courses that focus on applying GIS in industries that resonate with your interests, such as urban planning, environmental management, or public health.
  4. Hands-On Experience: Look for opportunities to gain practical experience, such as internships or research projects, where you can apply your GIS knowledge in real-world scenarios.

Building Your GIS Portfolio

Creating a portfolio of GIS projects is essential for showcasing your skills to potential colleges or employers. Your portfolio should include maps, data analysis reports, and any practical work you’ve undertaken during your coursework or internships.

Networking and Staying Informed

Stay connected with the GIS community by:

  1. Joining Student Organizations: Seek out or create GIS-related student organizations at your school to connect with peers who share your interests.
  2. Participating in Workshops: Attend GIS workshops or local meetups to network with professionals and gain insights into the practical applications of GIS.
  3. Online GIS Communities: Explore online GIS communities and forums to stay updated on industry trends and seek guidance from experienced practitioners.

Consider Ethical and Privacy Concerns

As a future GIS practitioner, it’s important to be aware of the ethical and privacy implications of working with geospatial data. Ensure you are prepared to handle sensitive information responsibly and in compliance with ethical standards.

Exploring Career Opportunities

A degree in GIS opens doors to a wide range of career opportunities, including GIS analyst, developer, manager, consultant, or specialist. Think about your long-term career goals and how you can contribute to the GIS field.

Conclusion

Embarking on a journey into the world of Geographic Information Systems can be an exciting and rewarding experience. By assessing your interests, pursuing relevant education, and actively participating in the GIS community, you can lay the foundation for a fulfilling career that combines your passion for geography and technology. Keep in mind that GIS is a dynamic field, so stay curious and adaptable as you pursue your dreams in this exciting domain.

Suggestion for Citation:
Amerudin, S. (2023). A Guide for School Students Interested in Pursuing a GIS Program. [Online] Available at: https://people.utm.my/shahabuddin/?p=7051 (Accessed: 9 September 2023).
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Location Privacy: Ensuring Control and Protection in an Evolving Digital Landscape

By Shahabuddin Amerudin

Abstract

In today’s interconnected world, location-based services have become an integral part of our daily lives. These services, which rely on various technologies such as satellite navigation systems, mobile carrier antennas, and wireless networks, enable us to navigate, communicate, and access a wide range of information. However, the pervasive use of location data raises significant concerns regarding location privacy. This article delves into the concept of location privacy, emphasizing the importance of individuals’ ability to control the disclosure and use of their location data. It explores the methods used to determine a device’s physical location and discusses the trade-offs between accuracy and power consumption. Additionally, this article highlights the impact of environmental factors on location accuracy. Through an academic lens, we seek to expand the discourse on location privacy, drawing on relevant research and academic perspectives.

Introduction

Location privacy, as defined by Beresford and Stajano, encompasses “the ability to prevent other parties from learning one’s current or past location.” This definition underscores the fundamental notion that individuals should retain agency over their location data and its subsequent use, extending the broader concept of privacy (Beresford & Stajano, 2003). In an era dominated by smartphones, Internet of Things (IoT) devices, and a proliferation of location-based applications, the significance of location privacy cannot be overstated. It lies at the intersection of technological advancement, personal autonomy, and ethical considerations.

Methods of Location Determination

1. Satellite Navigation Systems

One of the primary methods for determining a device’s physical location is through satellite navigation systems, most notably the Global Positioning System (GPS). GPS has revolutionized navigation, enabling users to pinpoint their location with remarkable accuracy. The European Space Agency (ESA) notes that GPS can achieve positioning accuracies of just a few centimeters when used in outdoor settings (European Space Agency, 2016). However, it is important to recognize that the accuracy of GPS can be significantly compromised when signals are obstructed by natural or man-made obstacles, such as mountains or buildings (Dardari et al., 2015).

2. Mobile Carrier Antennas

Mobile carrier antennas play a pivotal role in determining a device’s location, particularly in urban environments where GPS signals may be unreliable. These antennas triangulate the device’s position based on its proximity to cellular towers. While this method provides a reasonable level of accuracy, it is susceptible to inaccuracies arising from signal interference, network congestion, and the density of cellular infrastructure.

3. Wireless Networks

Wireless networks, including Wi-Fi and Bluetooth, also contribute to location determination. These technologies utilize signal strength and proximity to access points to estimate a device’s location. The advantage of wireless networks lies in their availability indoors and in areas with limited GPS coverage. However, like mobile carrier antennas, their accuracy can be influenced by various factors, including signal strength, interference, and the density of access points.

Accuracy vs. Power Consumption

The accuracy of location determination is a critical consideration in the context of location privacy. As Zhang et al. (2020) point out, devices can employ a combination of these methods to enhance accuracy. However, this comes at the cost of increased power consumption, which directly impacts the device’s battery life. Striking a balance between accuracy and power efficiency is an ongoing challenge for developers of location-based services. Achieving high accuracy while preserving battery life remains a key research area in the field of location privacy.

Environmental Factors

Environmental factors, such as physical obstructions and indoor environments, significantly affect the accuracy of location determination. As mentioned earlier, GPS accuracy can deteriorate when signals are obstructed by obstacles. Moreover, indoors, where GPS signals may not penetrate effectively, reliance on mobile carrier antennas and wireless networks becomes more pronounced. Researchers like Dardari et al. (2015) have explored techniques to improve location accuracy in challenging environments, shedding light on the complex interplay between technology and physical surroundings.

Conclusion

Location privacy is a multifaceted issue that intersects with technology, ethics, and individual autonomy. The methods employed to determine a device’s physical location involve trade-offs between accuracy and power consumption, making it imperative to strike a balance that aligns with user preferences and device capabilities. Moreover, environmental factors introduce complexities that demand innovative solutions to ensure reliable location determination in all scenarios. As location-based services continue to evolve, the academic community and industry stakeholders must collaborate to address these challenges and uphold the principles of location privacy.

In conclusion, location privacy is not merely a technical concern but a societal one, requiring ongoing research, ethical considerations, and the development of robust technologies to empower individuals to protect their location data.

References

  1. Beresford, A. R., & Stajano, F. (2003). Location Privacy in Pervasive Computing. IEEE Pervasive Computing, 2(1), 46-55.
  2. Dardari, D., Closas, P., Djurić, P. M., & Nannuru, S. (2015). Indoor Tracking: Theory, Methods, and Technologies. IEEE Journal of Selected Topics in Signal Processing, 10(1), 3-16.
  3. European Space Agency. (2016). Accuracy of GNSS. Retrieved from https://www.esa.int/Applications/Navigation/Galileo/Accuracy_of_GNSS
  4. Zhang, Y., Zhao, Z., Xu, W., & Liu, Y. (2020). A Survey on Smartphone-based Indoor Localization Techniques. IEEE Communications Surveys & Tutorials, 22(1), 466-490.
  5. Poikela, M. E. (2020). Perceived Privacy in Location-Based Mobile System. In A. Juan-Fita, V. Alhazov, M. Margenstern (Eds.), DNA Computing and Molecular Programming (pp. 115-126). Springer. doi:10.1007/978-3-030-34171-8
Suggestion for Citation:
Amerudin, S. (2023). Location Privacy: Ensuring Control and Protection in an Evolving Digital Landscape. [Online] Available at: https://people.utm.my/shahabuddin/?p=6970 (Accessed: 2 September 2023).
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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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Mengatasi Masalah Pengurusan Tanah Perkuburan melalui Inovasi Teknologi

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

Oleh Shahabuddin Amerudin

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

Cabaran dalam Pengurusan Tanah Perkuburan

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

Solusi Inovatif Melalui Laman Web Tanah Perkuburan Kangkar Pulai

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

Impak dan Kesimpulan

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

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

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

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

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

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

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

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

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

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

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

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

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

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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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Simplifying Automated Building Footprint Extraction with Deep Learning in GIS

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


By Shahabuddin Amerudin

Abstract

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

Automated Building Footprint Extraction via Deep Learning Techniques

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

Efficiency in Object Detection

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

Practical Implementation

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

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

Overcoming Challenges and Enhancing Results

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

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

Performance Advantages and Future Prospects

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

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

Reference

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

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

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

By Shahabuddin Amerudin

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

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

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

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

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

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


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

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

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

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

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

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

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

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

By Shahabuddin Amerudin

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

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

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

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

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

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

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

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

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

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

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

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

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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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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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Enhancing Smartphone GNSS Accuracy with External Receivers

By Shahabuddin Amerudin

In an era driven by digital navigation and geospatial accuracy, the fusion of smartphones and external GNSS receivers has revolutionized positioning capabilities. With the proliferation of location-based services, achieving sub-meter accuracy with smartphones has become a tantalizing possibility. In this article, we explore how to realize this feat, including a dive into cost-effective external receivers that can elevate your smartphone’s positioning prowess.

Smartphone GNSS: The Foundation

Modern smartphones are equipped with built-in GNSS modules that tap into constellations like GPS, GLONASS, Galileo, and BeiDou, providing basic positioning services. However, their inherent limitations, such as susceptibility to signal obstructions and variations in accuracy, have led to the exploration of supplementary solutions.

The Pursuit of Sub-Meter Accuracy

To bridge the gap to sub-meter accuracy, external GNSS receivers come into play. These devices are designed to amplify smartphone GNSS capabilities by leveraging advanced technologies and precision-focused designs. By coupling the external receiver with your smartphone, you’re set to unlock unparalleled accuracy across various applications.

Selecting the Right External Receiver

Cost-effectiveness is a key consideration, as professional-grade accuracy doesn’t have to come at exorbitant prices. Here are some external GNSS receivers that strike a balance between performance and affordability:

  1. Emlid Reach RS2: A popular choice in the surveying and mapping community, the Reach RS2 offers RTK functionality at a reasonable price point. With sub-centimeter accuracy and real-time corrections via LoRa radio, it empowers precision.
  2. Bad Elf GNSS Surveyor: This Bluetooth-enabled receiver is a versatile solution for sub-meter accuracy. It pairs seamlessly with smartphones and tablets, serving applications ranging from agriculture to outdoor recreational activities.
  3. Geneq iSXBlue RTN: Featuring real-time kinematic capabilities, the iSXBlue RTN offers a cost-effective entry into high-accuracy positioning. Its robust performance makes it a reliable companion for GIS data collection.
  4. Swift Navigation Piksi Multi: Combining affordability with centimeter-level accuracy, the Piksi Multi stands out with multi-constellation support and the flexibility to cater to a variety of industries.
  5. Trimble R1 GNSS Receiver: This compact device pairs with smartphones via Bluetooth, enhancing accuracy with sub-meter precision. Its rugged design suits outdoor environments.

Achieving Sub-Meter Accuracy: The Process

The journey toward sub-meter accuracy involves a sequence of steps:

  1. Research and Compatibility: Select an external receiver compatible with your smartphone’s operating system and connectivity options (Bluetooth, USB, etc.).
  2. Receiver Setup: Follow manufacturer guidelines to set up the external receiver. This typically involves connecting it to your smartphone and installing relevant apps or software.
  3. Signal Optimization: Ensure unobstructed line-of-sight to the sky. Minimize interference from surrounding structures and electronic devices.
  4. Augmentation Services: Some external receivers support correction services like SBAS/WAAS. Enable these services to enhance accuracy further.
  5. App Integration: Download and configure compatible apps that leverage the external receiver’s capabilities. These apps might provide real-time positioning data and error correction.
  6. Data Collection: Engage in data collection activities, leveraging the enhanced accuracy of the external receiver. Record and store data for analysis and mapping.

Balancing Performance and Budget

Cost considerations play a pivotal role when selecting an external receiver. While high-end solutions exist, an array of mid-range options empowers users with remarkable sub-meter accuracy without straining their wallets.

In your quest for sub-meter accuracy, the alliance between smartphones and external GNSS receivers holds transformative potential. The landscape of navigation, mapping, and surveying is evolving, and achieving pinpoint accuracy has never been more accessible. With a calculated approach to selecting the right receiver and optimizing your smartphone’s capabilities, you’re poised to navigate the realms of sub-meter accuracy in the palm of your hand.

Suggestion for Citation:
Amerudin, S. (2023). Enhancing Smartphone GNSS Accuracy with External Receivers. [Online] Available at: https://people.utm.my/shahabuddin/?p=6620 (Accessed: 14 August 2023).
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Unleashing Sub-Meter Accuracy with L1 and L5 GNSS Frequencies

By Shahabuddin Amerudin

Introduction

In the realm of modern navigation and geospatial technology, achieving unparalleled accuracy has become a driving force. As industries and individuals seek to unlock the potential of location-based services, the fusion of advanced GNSS technology with smartphones and external receivers has become a game-changer. One of the pivotal benchmarks in this quest is the pursuit of sub-meter accuracy – a feat that was once confined to specialized equipment but is now within reach through innovative approaches.

The confluence of two significant GNSS frequency bands, L1 and L5, stands as a beacon of hope for realizing sub-meter accuracy. These bands, each with its unique advantages, offer the promise of enhanced precision by addressing challenges like ionospheric delays and multipath interference. In this article, we embark on a journey to explore how the strategic combination of L1 and L5 frequencies, harnessed through dual-frequency GNSS receivers, holds the key to unlocking sub-meter accuracy. We delve into the science behind the synergy and the tangible benefits it brings to a range of applications that demand precision.

Let’s delve into how this can be accomplished:

  1. L1 and L5 Bands Overview
    • L1 Band (1575.42 MHz): This is the primary frequency used by most GNSS constellations (GPS, Galileo, BeiDou, and QZSS). L1 provides standard positioning accuracy, typically ranging from a few meters to around a meter.
    • L5 Band (1176.45 MHz): The L5 frequency is designed to improve accuracy and reliability. It is used by some modernized GNSS systems, such as GPS L5 and Galileo E5a. L5 offers better resistance to ionospheric disturbances and multipath interference, contributing to enhanced accuracy.
  2. Combining L1 and L5 for Sub-Meter Accuracy
    • Ionospheric Correction: The L5 band’s resistance to ionospheric delays is a key advantage. Ionospheric delays can cause inaccuracies in positioning, especially for signals in the L1 band. By combining L1 and L5 measurements, you can mitigate the effects of ionospheric delays and improve overall accuracy.
    • Multipath Reduction: Multipath interference occurs when GNSS signals reflect off surfaces before reaching the receiver. The L5 band’s design helps reduce multipath effects, contributing to more accurate positioning.
  3. Dual-Frequency Receivers
    • Achieving sub-meter accuracy with L1 and L5 involves using dual-frequency GNSS receivers. These receivers track signals from both L1 and L5 bands simultaneously.
    • Dual-frequency receivers leverage the different signal propagation characteristics of L1 and L5 to correct for ionospheric delays and multipath effects. This leads to more accurate position solutions.
  4. RTK and Precise Point Positioning (PPP)
    • Real-Time Kinematic (RTK) and Precise Point Positioning (PPP) techniques benefit significantly from dual-frequency GNSS receivers.
    • RTK involves receiving real-time corrections from a base station to achieve centimeter-level accuracy. Dual-frequency receivers enhance the accuracy of RTK solutions by leveraging L1 and L5 measurements.
    • PPP uses precise satellite orbit and clock data to achieve high accuracy without real-time corrections. Dual-frequency receivers improve PPP solutions by providing more accurate measurements to the positioning algorithm.
  5. Additional Considerations
    • While L1 and L5 combination enhances accuracy, the performance also depends on the overall quality of the GNSS receiver, signal environment, and the availability of augmentation services like SBAS.
    • Check whether your smartphone or external receiver supports tracking L1 and L5 signals and whether it can leverage the benefits of these frequencies for enhanced accuracy.

Conclusion

In the dynamic landscape of spatial data, the symbiosis of L1 and L5 GNSS frequency bands has ushered in a new era of precision. The pursuit of sub-meter accuracy, once a distant goal, has become a tangible reality for professionals and enthusiasts alike. By harnessing the power of dual-frequency GNSS receivers, we’ve uncovered a pathway to transform smartphones and external devices into instruments of remarkable accuracy.

As we conclude our exploration, it’s evident that the sub-meter accuracy achieved through L1 and L5 integration is not just a technical achievement, but a catalyst for innovation. It empowers sectors as diverse as surveying, agriculture, emergency response, and infrastructure management to operate with precision that was once unattainable. The convergence of technology and ingenuity, epitomized by the harmonious blend of L1 and L5 frequencies, has opened doors to possibilities that resonate across industries and applications.

In a world where accuracy is paramount, the amalgamation of L1 and L5 stands as a testament to human ingenuity’s capacity to redefine what’s achievable. As we navigate toward a future where sub-meter accuracy is the new normal, we’re reminded that the quest for precision knows no bounds – and the journey has only just begun.

Suggestion for Citation:
Amerudin, S. (2023). Unleashing Sub-Meter Accuracy with L1 and L5 GNSS Frequencies. [Online] Available at: https://people.utm.my/shahabuddin/?p=6618 (Accessed: 14 August 2023).
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Pioneering High-Accuracy GNSS Positioning with Low-Cost Receivers and Smartphones

By Shahabuddin Amerudin

Introduction

In the dynamic landscape of navigation and positioning technology, a remarkable breakthrough has emerged: the accessibility of high-accuracy GNSS positioning through low-cost receivers and smartphones (Marchi et al., 2022). As we delve deeper into this realm, we uncover a transformative era of precision that was once reserved for specialized equipment. This article explores the advancements, implications, and the potential for achieving centimeter-level accuracy through these affordable solutions.

Empowering Ubiquity

The narrative begins with the widespread adoption of low-cost GNSS technology. This innovation is now not only accessible but also adaptable with minimal investment. The EGSA GNSS Market Report (2019) offers compelling insights, forecasting an astonishing rise in the global installed base of GNSS devices – a leap from 6.4 billion in 2019 to a projected 9.6 billion by 2029. This growth mirrors the expanding annual GNSS receiver shipments, which are poised to escalate from 1.8 billion units in 2019 to an impressive 2.8 billion units by 2029. The surge in devices per capita from 0.8 to 1.1 globally signifies a world embracing the potential of accurate positioning.

Layers of Price Segmentation

The GNSS receiver landscape unfolds in distinct price tiers, characterized by their affordability and application scope, as delineated by EGSA (2019):

  1. Mass-market receivers – Priced below 5 €, these receivers dominate the market, constituting approximately 90 percent of shipments. Primarily catering to smartphones and wearables, they usher in precision for the masses.
  2. Rise receivers – Positioned in the price range of 5 € to 150 €, this segment showcases an impressive estimated annual growth rate of 6 percent. Their versatility spans unpowered assets, road infrastructure, and drone applications, fostering innovation in various sectors.
  3. High-end receivers – Anchoring the premium tier, these receivers command prices above 150 €. Despite accounting for less than 3 percent of total GNSS receiver shipments, they are the stalwarts of precision across diverse professional domains.

Elevating the GNSS Downstream Market

The burgeoning GNSS downstream market is fueled by revenues from mass-market and mid-end devices (<150 €), in tandem with augmentation services. Projections from EGSA (2019) underscore a monumental revenue surge, scaling from 150 € billion in 2019 to a staggering 325 € billion by 2029.

Redefining Smartphone and Low-Cost Receiver Performance

The heart of this transformation lies in the recalibration of performance benchmarks for low-cost GNSS solutions. High-end receivers, traditionally synonymous with dual signal frequencies (L1+L2), usher in centimeter-level accuracy and erode biases, underscoring their utility for precision-centric tasks. In contrast, the prevailing mass-market receivers, exemplified by the likes of u-blox, operate on a single signal frequency (L1), a configuration more sensitive to ionospheric perturbations. This sensitivity inherently influences performance, a challenge that modern advancements have confronted (Cina and Piras, 2015).

Eclipsing the Conventional

Smartphone Advancements: Modern smartphones have engineered a paradigm shift in the perception of attainable accuracy. The advent of u-blox and other single-frequency GNSS receivers for smartphones has shattered preconceived notions. Their capabilities, showcased vividly by Dabove et al. (2020), reverberate with astounding precision in static and kinematic positioning scenarios. The metamorphosis is anchored in an amalgamation of robust GNSS signals, infrastructural amplifications dedicated to GNSS positioning (spanning CORS, network, NRTK, and more), and an escalating interest from user communities and industry giants invested in top-tier positioning solutions (Dabove et al., 2020).

Expanding Horizons

Enhanced Positioning Strategies: The confluence of mass-market receivers with the offerings of GNSS permanent station networks yields a fertile ground for innovation. Cina and Piras (2015) exemplify this synergy with their groundbreaking work on precise and cost-effective landslide monitoring. This fusion elevates accuracy and affordability, driving progress across diverse applications.

The Unveiling of Precision

Comparative Performance Insights: Drawing a comparative lens between modern smartphones and u-blox GNSS receivers in real-time and post-processing contexts yields compelling revelations. The u-blox receiver consistently secures an upper hand, attaining precision levels within a remarkable 5 cm and accuracy honed to an exceptional 1 cm. Meanwhile, smartphone outcomes present slight disparities, often ranging in the order of meters due to the inherent noise within measurements (Dabove et al., 2020). Despite this, the potential for reliable positioning using these technologies remains undeniably promising. Enhancing this potential further lies the prospect of integrating advanced external antennas, propelling the realm of performance to even greater heights (Dabove et al., 2020).

Conclusion

The voyage into the world of high-accuracy GNSS positioning through low-cost receivers and smartphones has embarked upon a new trajectory. What was once a realm reserved for specialized devices has now been democratized, ensuring that centimeter-level accuracy is within reach for myriad applications. The implications ripple across sectors, reshaping possibilities and redefining precision.

References

Cina, A. and M. Piras (2015). Performance of low-cost GNSS receiver for landslides monitoring: Test and results, Geomatics, Natural Hazards and Risk, 6(5-7): 497-514. 

Dabove, P., V. Di Pietra and M. Piras (2020). GNSS Positioning Using Mobile Devices with the Android Operating System, ISPRS International Journal of Geo-Information, 9(4): 220. 

EGSA (European Global Navigation Satellite Systems Agency) (2019). GNSS Market Report. Issue 6; Retrieved from https://www.gsa.europa.eu/market/market-report; accessed on 20 April, 2020. 

Marchi, M. D., Diantini A. and Pappalardo, S.E. (2022). Drones and Geographical Information Technologies in Agroecology and Organic Farming. CRC Press.

Suggestion for Citation:
Amerudin, S. (2023). Pioneering High-Accuracy GNSS Positioning with Low-Cost Receivers and Smartphones. [Online] Available at: https://people.utm.my/shahabuddin/?p=6611 (Accessed: 14 August 2023).