Geographic Information System Technology Research Articles

Design and Implementation of Time Point Approval and Dynamic Monitoring for Rural Illegal Occupation of Farmland for Constructing Houses

Abstract. The illegal occupation of farmland for building houses has impact not only on the land resources, but also the agricultural production, ecological environment, and food security. In order to protect farmland resources, maintain stable agricultural production activities and improve ecological environment, this paper designs and implements a time-point approval and dynamic monitoring solution and system for the problem of rural illegal occupation of farmland for constructing houses by comprehensively utilizing the satellite remote sensing technology (RS) and geographic information system technology (GIS). The result shows that the time-point approval and dynamic monitoring system can identify the illegal construction of houses accurately, thus this work can curb the generation of new illegal houses, and support the special governance of the construction of illegally occupied farmland effectively and continuously.

An analysis of qualifications and requirements for geographic information systems (GIS) positions in the United States

AbstractAs the demand for geospatial analytics continues to grow, geographic information systems (GIS) professionals are needed to build, operate, and maintain GIS technologies, data, and software to provide geospatial insights for modern industries and organizations. To best train the next generation of GIS professionals, an understanding of qualifications and requirements of GIS positions is needed. Thus, this work analyzes 508 GIS positions, grouped by position type (analysts, developers, educators, managers, specialists, technicians) to provide insights on key pre‐requisite requirements, such as education, experience, certifications, soft communication skills, programming skills, and knowledge of GIS or IT. In general, possession of a bachelor's degree in GIS, geography, or computer science, prior professional experience, and knowledge of GIS and IT software were common pre‐requisites for most GIS roles. Soft communication skills were also frequently desired for GIS roles. We also found that some position requirements tended to vary by position type, such as manager and developer roles requiring on average 5 years or higher prior experience, while analyst, specialist, and technician roles had much lower experience and education requirements. Higher education institutions and GIS training programs should note the desired requirements for GIS position types and continue to refine programs and develop pathways for success for aspiring GIS professionals.

Population analysis using GIS software tools A case study of Chengdu

Understanding how the population is distributed geographically helps governments and organizations allocate resources more efficiently. For example, they can allocate funds for infrastructure development, healthcare facilities, schools, and public services based on the population's concentration and distribution. City planners can use population distribution data to design cities and urban areas effectively. They consider factors like housing needs, transportation infrastructure, and zoning regulations based on the population's spatial distribution. This study takes Chengdu as an example, and the article analyzes the spatial structure distribution of the population in Chengdu in the last decade based on the census data of 2012, 2017, and 2021, using analysis methods such as population distribution structure index (PDSI), mean pointer center, and population center of gravity, combined with Geographic Information System (GIS) technology. According to the analysis, it can be concluded that the population distribution pattern in Chengdu has not changed much, but the city area has been expanded. Besides, the policy adjustment has a certain impact on the population center of gravity but not as much as other factors. At last, topography, transportation, and economic development all have an impact on the population distribution of Chengdu.

Scalable business models for startups in renewable energy: Strategies for using GIS technology to enhance SME scaling

The renewable energy sector has witnessed remarkable growth in recent years, driven by global efforts to mitigate climate change and transition towards sustainable energy sources. Startups play a crucial role in this transition, often pioneering innovative solutions and technologies. However, for these startups to thrive and contribute significantly to the renewable energy landscape, scalable business models are essential. This abstract explores strategies for leveraging Geographic Information System (GIS) technology to enhance scalability in small and medium-sized enterprise (SME) startups operating in the renewable energy sector. GIS technology offers a powerful framework for analyzing and visualizing spatial data, providing valuable insights into site selection, resource assessment, and infrastructure planning. By integrating GIS into their business models, startups can optimize operations, reduce costs, and accelerate growth. This review discusses various strategies for utilizing GIS technology, including market analysis, site suitability assessment, supply chain optimization, and infrastructure planning. Drawing on case studies and industry examples, this review highlights the effectiveness of GIS in enhancing scalability for renewable energy startups. It also addresses the challenges and limitations associated with GIS implementation, such as technical complexities, data accessibility, and cost considerations. Furthermore, this review discusses emerging trends in GIS technology and their potential impact on the renewable energy sector, offering insights into future opportunities for startups. In conclusion, this review emphasizes the importance of GIS technology in facilitating scalable business models for renewable energy startups. By harnessing the power of GIS, SMEs can unlock new growth opportunities, drive innovation, and contribute significantly to the transition towards a sustainable energy future. Keywords: Scalable Business Models, Startups, Renewable Energy, GIS Technology, SME Scaling.

Using cluster analysis to identify communities of interest for purposes of legislative redistricting: A case study of parishes in Louisiana

The use of Geographic Information Systems (GIS) technology has been found to limit the kinds of communities that can in principle achieve political representation through redistricting because the process excludes “communities of interest” (COIs) that cannot be mapped. We argue that these limits can be overcome using empirically based variables to define a COI in conjunction with an empirically based classification system, cluster analysis. Our argument is based on a case study of a legislative redistricting case in which we examined the cultural, economic, demographic, historic, and social characteristics upon which a COI would be defined. We employed an empirically and scientifically based classification system called “cluster analysis” to determine if these two geographically-separated groups of parishes form a single COI, which, in turn, would serve as a justification for them being placed together into reorganized congressional District 5. We find that East Carroll Parish and its six neighboring parishes (Franklin, Madison, Morehouse, Richland, Tensas, and West Carroll) are in a different COI grouping than East Baton Rouge Parish. Moreover, from the COI perspective, East Baton Rouge Parish should not be included in proposed plans involving a proposed RCD5 that include East Carroll Parish and its six neighboring parishes. This finding also applies to Lafayette Parish, both in whole and in part. This finding is not only relevant because COIs are important in redistricting, but it demonstrates that the limits identified in regard to the use of GIS-based technology in identifying COIs can be surmounted.

Quantification of Soil Erosion and Sediment yield using the RUSLE model in Boyo Watershed, Central Rift Valley Basin of Ethiopia

Changes in land use and land cover (LULC) are becoming recognized as critical to sustainability research, particularly in the context of changing landscapes. Soil erosion is one of the most important environmental challenges today, particularly in developing countries like Ethiopia. The objective of this study was evaluating the dynamics of soil loss, quantifying sediment yield, and detecting soil erosion hotspot fields in the Boyo watershed. To quantify the soil erosion risks, the Revised Universal Soil Loss Equation (RUSLE) model was used combined with remote sensing (RS) and geographic information system (GIS) technology, with land use/land cover, rainfall, soil, and management approaches as input variables. The sediment yield was estimated using the sediment delivery ratio (SDR) method. In contrast to a loss in forest land (1.7%), water bodies (3.0%), wetlands (1.5%), and grassland (1.7%), the analysis of LULC change (1991-2020) showed a yearly increase in the area of cultivated land (1.4%), built-up land (0.8%), and bare land (3.5%). In 1991, 2000, and 2020, respectively, the watershed's mean annual soil loss increases by 15.5, 35.9, and 38.3 t/ha/y. Approximately 36 cm of the watershed’s economically productive topsoil was lost throughout the study's twenty-nine-year period (1991-2020). According to the degree of erosion, 16% of the watershed was deemed seriously damaged, while 70% was deemed slightly degraded. Additionally, it is estimated for the year 2020 that 74,147.25 t/y of sediment (8.52% of the total annual soil loss of 870,763.12 t) reach the Boyo watershed outlet. SW4 and SW5 were the two sub-watersheds with the highest erosion rates, requiring immediate conservation intervention to restore the ecology of the Boyo watershed.

Assessing groundwater quality in semi-arid conditions: a geographic information systems-integrated approach using water quality index

Meeting freshwater demands in water-scarce regions is imperative, given challenges like hydric stress, urbanization, aridity, and population growth. This study focuses on the Tebessa aquifer system, a critical water source facing escalating demands. A primary objective is to identify actionable management strategies to improve groundwater quality dynamics in semi-arid regions. The methodology involves a comprehensive assessment of groundwater health using the Water Quality Index (WQI) approach. This method integrates diverse parameters influenced by intensive agricultural and industrial activities. Additionally, spatial relationship analysis, facilitated by Geographic Information System (GIS) technology, is employed to gain insights into complex hydrogeological processes. The multi-parameter strategy implemented results in water quality maps that highlight various ions, revealing spatial disparities and assigning quality priority classes from 'Good' to 'Poor' across the groundwater. The research, conducted in the semi-arid Tebessa-Morsott Plain, encompasses field and laboratory investigations to delineate hydrochemical traits, including alkalinity, salinity, and heightened ion concentrations. The results of the study contribute to scientific understanding by uncovering interrelationships and spatial intricacies, offering valuable insights for water resource governance. The effectiveness of the methodology, incorporating WQI and GIS, is demonstrated in the comprehensive evaluation of groundwater quality. Ultimately, this research provides a foundation for informed decision-making and sustainable management of groundwater resources in semi-arid regions.

Modelling and Mapping of Aboveground Carbon of Oluwa Forest Reserve Using LandSat 8 TM and Forest Inventory Data

This study was conducted in Oluwa Forest Reserve to assess and predict its aboveground carbon sequestration potentials using LandSat Thematic Mapper data. The Oluwa Forest Reserve, Ondo State, Nigeria, is recognized for its rich biodiversity and extensive size. To estimate its forest aboveground biomass and carbon should be complex and costly endeavour requiring the expertise of various professionals and equipment. Consequently, this study explored the use of Geographic Information System (GIS) and Remote Sensing (RS) technology using LandSat bands to estimate spectral indices in fitting linear models to predict the aboveground carbon sequestration potentials of the tropical rainforest ecosystem of Oluwa Forest Reserve. The observed aboveground carbon from sample plots and the estimated spectral indices were used to model the spread of aboveground carbon of Oluwa Forest Reserve. Positive linear relationship exists between the observed and the spectral indices data estimated. Therefore, linear models were fitted and the best-fit was determined using statistical measures. The aboveground carbon average estimated from the sample plots and the predicted were 150.70 t/ha and 149.80 t/ha, respectively. The coefficient of determination 94% and Root Mean Square Error = 6.38E-16, respectively were obtained statistically. The chosen model predicts the aboveground carbon spread of Oluwa Forest Reserve adequately. The study revealed that spectral data, GIS and RS are critical for large forest aboveground carbon modelling and mapping for efficiency.

Smart Garden Management System Based on the combination of Internet of Things and Geographic Information System Technology

The Internet of Things (IoT) technology is inevitably forging ahead in a number of areas all among them being the agricultural sector and in particular the affected environment. This paper utilizes the farm's IoT where through an effective analysis, the smart garden is designed and managed. It blends field research, project implementations and theoretical analysis to improve the case implementation. It can be considered as more rigorous and practical. The study intends, first and foremost, to look at agricultural IoT based technologies and how IoT changes the way farmers work and agricultural landscapes look. Different theories have been used in the implementation of these concepts which include agriculture, tourism, and landscape design. The technical side will integrate the Internet of Things technology, the adaptation theory of agricultural industrialization, the formation of the theory of ecotourism, the application of tourism psychological theory in tourism discipline, the ecology theory of landscape and aesthetics theory, and landscape gardening planning and design theory. A comprehensive examination of the smart park has been conducted, including relevant theories and the application of planning and design principles. This systematic research aims to provide scientific direction for the agricultural aspects of the smart park. In order to offer scientific direction for the planning and design of agricultural Internet of Things (IoT) in smart gardens, this study presents a theoretical framework for the planning and design of smart gardens that incorporate agricultural IoT. The framework is comprehensively explained, encompassing its concept, distinguishing features, relevant theories, and guidance approaches.

Suitability of Site Selection for Mountain Railway Engineering Spoil Disposal Areas from a Multi-Scenario Perspective

The current approach to selecting sites for abandoned spoil areas primarily relies on qualitative methods, often overlooking the impact of policy factors on decision-making. Traditional single-site selection strategies may not be flexible enough to accommodate evolving external policy demands. Addressing this challenge is crucial for ensuring the site selection for abandoned spoil areas is both scientifically sound and policy-compliant. This research integrates various analytical methods, including principal component analysis, complex network theory, the CRITIC method, and the ordered weighted averaging method, to thoroughly evaluate the factors influencing site selection. Utilizing geographic information system (GIS) technology, the study simulates different policy scenarios, such as construction cost, social and ecological concerns, natural security, spatial accessibility, and a comprehensive balance approach. It specifically analyzes the suitability of the spoil site of a segment of the Chongqing ZW Railway under these policy conditions. Based on the actual policy situation in the local area, six potential suitable sites were screened with the help of field investigation. This study can offer a methodological framework and theoretical guidance for optimally locating mountain railway engineering waste disposal sites. In addition, the methodology presented in this study can be adapted to the development and change in policy scenarios.

Groundwater flow simulation for artificial recharge: a GIS, remote sensing, and MODFLOW integration

Abstract Groundwater (GW) emerges as a sustainable resource to meet water demand, yet current GW reservoirs fall short of projected population needs. Artificial GW recharge, bolstered by Geographical Information System (GIS) and Remote Sensing (RS) technologies, stands out as a pivotal solution. Examining Aurangabad district in Maharashtra, India the study uncovers distinct challenges: Aurangabad faces escalating water demand due to population growth, leading to GW over-exploitation. Leveraging hydrogeological parameters, rainfall analysis, and GW availability, the study proposes tailored GW recharge techniques. Utilizing MODFLOW simulation, the study explores diverse recharge rates and existing GW availability, showcasing promising results. Integration of stream augmentation, bladder pillow tank methods, and dug well recharge is examined via GIS and RS techniques. Simulation outcomes project significant GW level increases (11.55–23.70 m) with varying recharge rates, effectively meeting region-specific water demand projections. The findings underscore the imperative of sustainable water management practices and technological advancements in addressing water scarcity crises.

Estimating Accident Reduction Rate after Maritime Traffic Safety Assessment Using Synthetic Minority Oversampling Technique and Machine Learning Algorithm

This study was focused on deriving the MTSA-related accident reduction rate (ARR) required to calculate the safety benefits before and after expanding the scope of the system. By performing spatial analysis using geographic information system technology, MTSA-related accidents were identified on maritime routes near both assessed and unassessed project sites from 2010 to 2014. Subsequently, by applying the synthetic minority oversampling technique to balance the data, the algorithm learned from the random forest using the operational data of coastal passenger ship operations and accident data near unassessed locations where MTSA is not implemented. Then, the trained model was applied to predict accident occurrence in the absence of MTSA near the latest operational information of coastal passenger ship operations at the assessed project sites. The MTSA-related ARR was then calculated by applying the actual accident occurrences during operation near the assessed project sites where MTSA was implemented. The MTSA-marine ARR calculated at 17.41% can be applied to the calculation of safety benefit for MTSA. The results of this study can provide quantitative evidence for the application of higher-level systems considering the burden on regulatory targets when improving MTSA or similar systems.

Inventory, Dynamic Evolution, and Scenario Projections of Agricultural Carbon Emissions in Shandong Province, China

The reduction in agricultural carbon emissions (ACEs) in Shandong Province is essential to China’s carbon peak and carbon neutrality objectives. In this regard, we constructed an ACE inventory for Shandong Province at a resolution of 1 km × 1 km, integrating the emission factor method with geographic information system (GIS) technology. Building upon this, we explored the dynamic evolution patterns of ACEs using kernel density estimation and conditional probability density estimation. Additionally, long short-term memory networks were trained to predict ACEs under various scenarios. The results showed that: (1) ACEs in Shandong Province exhibited two stages of change, i.e., “rise and decline”. Notably, 64.39% of emissions originated from the planting industry. The distribution of emissions was closely correlated with regional agricultural production modes. Specifically, CO2 emissions were predominantly distributed in crop cultivation areas, while CH4 and N2O emissions were primarily distributed in livestock breeding areas. The uncertainty of the emission inventory ranged from −12.04% to 10.74%, mainly caused by emission factors. (2) The ACE intensity of various cities in Shandong Province is decreasing, indicating a decoupling between ACEs and agricultural economic growth. Furthermore, the emission disparities among different cities are diminishing, although significant spatial non-equilibrium still persists. (3) From 2022 to 2030, the ACEs in Shandong Province will show a continuous downward trend. By 2030, the projected values under the baseline scenario, low-carbon scenario I, and low-carbon scenario II will be 6301.74 × 104 tons, 5980.67 × 104 tons, and 5850.56 × 104 tons. The low-carbon scenario reveals greater potential for ACE reduction while achieving efficient rural economic development and urbanization simultaneously. This study not only advances the methodology of the ACE inventory but also provides quantitative references and scientific bases for promoting low-carbon, efficient, and sustainable regional agriculture.

Geospatial Analysis of Archaeological Sites in Ikara Local Government Area of Kaduna State, Nigeria

Ikara Local Government Area of Kaduna State, Nigeria has rich and diverse archaeological heritage. The research employed geospatial technologies, including Geographic Information Systems (GIS) and remote sensing, to analyze and document the archaeological sites in Ikara. The research aims to assess the Geospatial distribution of Archaeological sites in the study area by mapping to locate the Archaeological sites in the study area, determine the pattern of distribution of the Archaeological sites and examine the finds and features in the sites. The research employed a mixed-method approach that combines field surveys, archival research, and geospatial analysis. The research findings show fifteen (15) archaeological sites dispersedly distributed across the Eight (8) wards in the study area with Ikara ward having the highest number of archaeological sites. Also, the dominant cultural materials across the sites are finds in forms of potsherds and features in form of House foundation. The cultural remains on the Archaeological sites are House foundations, mounds, granary, potsherds, iron slag, open grave, stone tools and grinding stones. Based on the findings, dispersed pattern of the Archaeological sites in the study area was deduced from the data collected especially at the southern part of the area of study where the sites are found to be most concentrated. Base on the research findings, it is recommended that further research shall be undertaken using GIS and Remote Sensing Technology for effective Archaeological site discovery, management and preservation.

Assessing road vulnerability in seismic conditions: a comprehensive study

Road infrastructure is indispensable for societal functionality, yet it is highly susceptible to the devastating impacts of seismic events. This paper focuses on enhancing the resilience of road systems by systematically identifying, quantifying, and assessing factors contributing to their vulnerability during earthquakes. The core objective of this study is to develop and validate a seismic vulnerability index for road sections, which aims to evaluate and classify the susceptibility of road infrastructure to earthquake-induced damage comprehensively. This systematic approach is pivotal for guiding effective mitigation strategies and prioritization efforts. To fulfill this objective, the study employs the Analytic Hierarchy Process (AHP) to introduce a novel methodology for calculating the seismic vulnerability index, incorporating various factors that influence road vulnerability. This method allows for the accurate classification of road sections into distinct levels of susceptibility, providing a solid foundation for implementing targeted interventions and enhancing road resilience. Further, the paper validates the theoretical model through several case studies, demonstrating the practical applicability and effectiveness of the seismic vulnerability index in real-world scenarios. Additionally, the use of Geographic Information System (GIS) technology to simulate earthquake scenarios within an urban road network offers valuable insights into the potential seismic behavior of roads. These simulations are crucial for identifying critical areas that require intervention and for planning resilience-building efforts more effectively. By proposing a comprehensive framework that combines rigorous analysis, empirical validation, and advanced simulation techniques, this paper seeks to make a significant contribution to the field of infrastructure resilience. It aims to advance the understanding of road vulnerability in seismic conditions and supports the development of strategic approaches for enhancing the resilience of road infrastructure against earthquakes. Through the development and validation of a seismic vulnerability index, this study meets its primary objective, providing a valuable resource for researchers, policymakers, and practitioners in disaster management and infrastructure planning.

Remote sensing estimation of regional PM2.5 based on GTWR model -A case study of southwest China

Air pollution in China has becoming increasingly serious in recent years with frequent incidents of smog. Parts of southwest China still experience high incidents of smog, with PM2.5 (particulate matter with diameter ≤2.5 μm) being the main contributor. Establishing the spatial distribution of PM2.5 in Southwest China is important for safeguarding regional human health, environmental quality, and economic development. This study used remote sensing (RS) and geographical information system (GIS) technologies and aerosol optical depth (AOD), digital elevation model (DEM), normalized difference vegetation index (NDVI), population density, and meteorological data from January to December 2018 for southwest China. PM2.5 concentrations were estimated using ordinary least squares regression (OLS), geographic weighted regression (GWR) and geographically and temporally weighted regression (GTWR). The results showed that: (1) Eight influencing factors showed different correlations to PM2.5 concentrations. However, the R2 values of the correlations all exceeded 0.3, indicating a moderate degree of correlation or more; (2) The correlation R2 values between the measured and remote sensed estimated PM2.5 data by OLS, GWR, and GTWR were 0.554, 0.713, and 0.801, respectively; (3) In general, the spatial distribution of PM2.5 in southwest of China decreases from the Northeast to Northwest, with moderate concentrations in the Southeast and Southwest; (4) The seasonal average PM2.5 concentration is high in winter, low in summer, and moderate in spring and autumn, whereas the monthly average shows a “V” -shaped oscillation change.

Landslide susceptibility modelling in the Doda Kishtwar ramban (DKR) region of Jammu and Kashmir using remote sensing and geographic information system

The present study represents a significant understanding, employing cutting-edge Remote Sensing(RS) and Geographic Information System (GIS) technologies by conducting a comprehensive landslide susceptibility modelling in the Doda, Kishtwar, and Ramban (DKR) districts of the Union Territory of the Jammu and Kashmir in the NW Himalaya. The primary objective of this study is to determine the areas that are at high risk of landslides and accordingly classified these risk zones into five distinct categories, ranging from very high (VH) to very low (VL) susceptibility. To achieve this, a multifaceted approach, involving the utilization of various terrain thematic layers in the GIS environment was applied. The preferred framework employed in this study is the Weighted Overlay (WO) analysis. The final outcome of the study is the final landslide susceptibility map which is a significant and valuable resource for a wide range of stakeholders, including decision-makers, land managers, and local communities. It equips these stakeholders with the information and insights required to adopt proactive strategies in response to the identified landslide susceptibility zones.

Spatial and Temporal Analysis: A GIS-Based Application Tool for Crime Monitoring and Clustering in Malawi

For the purposes of monitoring, evaluating, and conducting a geographical analysis of crime-related data, the study used geospatial technology to collect crime data based on spatial location and the Malawi Police Data Digest of 2019 and 2020. In a more generic sense, knowing the geographic patterns of crime in Malawi using GIS technology can help determine how to make and implement important decisions to reduce crimes in Malawi. The Malawi Police Service has established a number of database management systems to help with crime monitoring. Notwithstanding, it has not yet fully integrated Geographic Information Systems across all jurisdictions. Maps showing crime locations and crime hotspot zones are therefore not included in the crime data and statistics report provided by the Malawi Police Service. In this light, a lot of people have become victims of various forms of crimes in areas where those crimes are also prevalent. To collect, track, and analyze crime data in Malawi for this study, Geographical Information System (GIS) particularly network analysis techniques were used. Network Analysis was used to identify crime hotspots by analyzing crime data as a network of interconnected events and locations. The rationale behind this was to treat each crime event as a node in the network and the spatial relationships between the crimes as edges. By analyzing this network, patterns and relationships between crime events were revealed, allowing for the identification of crime hotspots. The study found that Lilongwe in the central region and the capital city registered the highest number of crimes seconded by Blantyre in the southern region and followed by Mangochi. Mzimba recorded high crimes in the northern region. In Malawi, the traditional systems of intelligence and criminal record keeping have failed to satisfy the demands of today's crime situation. Manual methods neither give accurate, dependable, or complete data 24 hours a day nor do they help in trend forecasting and decision assistance. It also leads to poorer productivity and inefficient workforce use. The appropriate application of information technology is the solution to this ever-increasing challenge.

Applying an Analytic Hierarchy Process and a Geographic Information System for Assessment of Land Subsidence Risk Due to Drought: A Case Study in Ca Mau Peninsula, Vietnam

The increase in extreme weather events causes secondary hazards that can influence people and the environment enormously. The Ca Mau Peninsula is known as one of the areas most severely affected by drought, and excessive groundwater exploitation is one of the reasons leading to a higher risk of land subsidence. This study uses the Delphi method and the KAMET rule table to analyze and select indicators that affect subsidence. The study uses the analytic hierarchy process (AHP) analytical hierarchy method to evaluate the weights of influencing factors, combined with geographic information system (GIS) technology to overlay the map layers of the main influencing factors and build a subsidence risk warning zoning map of the study area. The influencing factors selected to evaluate the impact on land subsidence in the study area during the drought period included geological structure, soil characteristics, groundwater flow exploitation, water flow in the dry season, current land use status, and evaporation in the dry season. The weights of these factors were evaluated based on the synthesis of relevant documents as well as consultation with experts. The results indicate that nearly two-thirds of the Ca Mau Peninsula area is currently at very low or low risk of subsidence. Meanwhile, 23% of the area is at medium risk, nearly 9% is at high risk, and 0.1% of the study area is at very high risk. Subsidence risk warning zoning maps can provide a visual and general overview of areas with high subsidence risk, supporting managers in making reference plans for socio-economic development in the Ca Mau Peninsula.

Kaedah dan Kegunaan dalam Perlaksanaan Integrasi BIM-GIS dan Potensinya dalam Pengurusan Sistem Saliran: Satu Tinjauan

The ability of Geographic Information System (GIS) applications to manage three-dimensional (3D) data is increasing along with advancements in current technology, and this ability can be further enhanced through the integration of Building Information Modeling (BIM) technology. While the combination of BIM and GIS technologies provides significant benefits to human life, there are several challenges and limitations that need to be addressed for successful implementation. Therefore, research on previous articles will be conducted to examine the challenges and potential of this integration. This paper will begin with searching for relevant previous articles using appropriate keywords on Web of Science and Scopus. The search will be conducted in two parts, articles related to BIM-GIS integration with keywords "BIM" AND "GIS," and articles concerning the usage of 3D data in GIS applications for drainage system management using keywords "GIS" AND "3D" AND "DRAINAGE." From these articles, observations will be made regarding (1) the structure of BIM-GIS integration data, (2) implemented integration methods, (3) utilization of integration models, and (4) the potential of BIM-GIS Integration in managing 3D drainage system data. The research identifies key challenges in BIM-GIS integration, including differences in class numbers, dissimilar class arrangement, geometry creation methods, and georeference system application. To address these challenges, standard protocols based on specific requirements need to be established by considering organizational needs to ensure a consistent integration process. The paper also recognizes significant potential in employing BIM-GIS integration for drainage system management.