Geospatial Information Systems Research Articles

A planar graph cluster‐routing approach for optimizing medical waste collection based on spatial constraint

AbstractMedical Solid Wastes (MSWs) are major hazardous materials containing harmful biological or chemical compounds that present public and environmental health risks. The collection and transportation of waste are usually informed by optimized work‐balanced routing based on comprehensive spatial data in urban traffic networks, called a Vehicle Routing Problem (VRP). This may be unsuitable for MSWs as their special category means they impose additional complexity. The present article develops a planar graph‐based cluster‐routing approach for the optimal collection of MSWs informed by a Geospatial Information System (GIS). The problem is first formulated as a mixed integer linear program in road network spatial data, in the context of Tehran city. The work has two key aims: (i) to minimize the total routing cost of MSW collection and transfer to waste landfills; (ii) to balance workload across waste collectors. There are three main contributions of the proposed approach: (i) to simplify the large search space area by converting the road network to a planar graph based on graph theory, spatial parameters, and topological rules; (ii) to use a modified K‐means algorithm for clustering; (iii) to consider average traffic impacts in the clustering stage and momentary traffic in the route planning stage. A planar graph extraction procedure is applied to capture the network sketch (i.e., a directed graph) from the traffic roadway network. An iterative cluster‐first‐route‐second heuristic is employed to solve the proposed routing problem. This heuristic customizes a K‐means algorithm to determine the optimal number and size of clusters (i.e., routes). A Traveling Salesman Problem (TSP) algorithm is applied to regulate the optimal sequence of visits to medical centers. The experimental results show improvements in balancing collectors' workload (i.e., ~4 min reduction in the standard deviation of average travel time) with reductions in travel time (i.e., an average ~1 h for the entire fleet and ~4 min per route). These findings confirm that the proposed methodology can be considered as an approach for optimizing waste collection routes.

Pemetaan Sarana dan Prasarana Objek Geowisata Batu Angus di Kecamatan Tarnate Barat Provinsi Maluku Utara

Batu Angus Geotourism in West Ternate District, North Maluku, is one of the popular tourist destinations in Indonesia that offers unique and interesting natural scenery. The area is famous for its black volcanic rocks and rough texture, the result of an ancient volcanic eruption. This study aims to map the facilities and infrastructure at the Batu Angus Geotourism Object in the Kulaba Village, West Ternate District using a GIS application in order to map the location and area of facilities and infrastructure, analyze the condition of facilities and infrastructure, and provide recommendations for development facilities and infrastructure at the Batu Angus Geotourism Object. The method used is a spatial approach based on a geospatial information system by means of field surveys to collect spatial data on the location and area of facilities and infrastructure, interviews with tourist attraction managers and tourists to collect attribute data on the condition of facilities and infrastructure, and documentation to collect secondary data in the form of photos and maps. The results of the study showed that the condition of the facilities and infrastructure was adequate, including a parking area, stage, gazebo, and toilets. Facilities that need to be improved include clean water, electricity, a museum, and road access. Mapping was carried out using the ArcGIS application to produce a digital map of the distribution of facilities and infrastructure. This map helps tourists to find available facilities. Batu Angus Geotourism has great potential to be developed. It is necessary to improve facilities and infrastructure, tourism promotion, and public awareness to support the development of this tourism.

Investigating the Influence of Environmental Factors on Corrosion in Pipelines Using Geospatial Modeling

This study integrated geographic information system (GIS) and remote sensing technology to identify areas around pipelines that are more susceptible to corrosion having the Kurdistan pipeline as a case study. Geospatial data are used to target factors such as rainfall, temperature, rivers, and minerals which increase the corrosion rate. Spatial data such as, the direction of slope, rainfall, proximity to rivers, and minerals, were collected and analyzed; maps were created for every individual factor to visualize their distribution. By overlaying these maps, regions that are at higher risk of corrosion were identified, which can be prioritized for further investigation or preventive measures. This paper’s findings are significant for oil and gas industries, including pipeline operators and designers as corrosion can lead to devastating consequences. The novelty of this study is to identify areas along the pipeline at higher risk of corrosion through the application of geospatial information systems and remote sensing. This methodology holds immense potential for industries looking to proactively prevent corrosion through the implementation of preventative maintenance, monitoring programs, and the application of protective coatings and inhibitors. The results of this research demonstrate that environmental data, GIS, and remote sensing can predict corrosion in oil pipelines, offering valuable insights for better managing corrosion risk.

Achieving Privacy-preserving Trajectory Query in Geospatial Information Systems with Outsourced Cloud

Achieving Privacy-preserving Trajectory Query in Geospatial Information Systems with Outsourced Cloud

Tackling sustainable development goals through new space

Achieving the UN's Sustainable Development Goals (SDGs) constitutes a formidable challenge. Existing solutions may be insufficient to respond to the scale and scope of the endeavour. The 17 SDGs are not discrete but interconnected, sustained by 169 targets. Their cross-level effects require the adoption of a panarchical view of data. New Space projects, still unfamiliar to many managers and organizations, provide such data related to grand challenges capable of addressing the paradoxes that arise from the interaction of a system of systems of multiple scales of spatiality, temporality and social organization. To address these requires project managing developing capabilities that can connect everyday interventions in terrestrial economy and society with high level data findings from Geospatial Information Systems. We contribute to the SDG debate through the articulation of three streams of literature that may radically revise the way wicked problems are addressed: panarchy, paradox, and New Space.

Flood inundation assessment of UNESCO World Heritage Sites using remote sensing and spatial metrics in Hoi An City, Vietnam

Flooding is the most frequent and damaging threat to The United Nations Educational, Scientific, and Cultural Organization (UNESCO) World Heritage Sites, and has been exacerbated by climate change. Hoi An Ancient Town, one of the world's cultural heritage sites in Vietnam, is facing inundation risks from increasing extreme flood events due to anthropogenic and natural processes. This study combines Geospatial Information System (GIS), remote sensing data, and landscape ecology metrics to assess the flood risk of Hoi An City. The analysis includes (1) classification of land-use/land-cover (LULC) using Sentinel-2 data; (2) computation of a flood risk index using hazard characteristics, physical and demographic exposures, and physical and socioeconomic vulnerabilities, together with adaptive capacity and spatial metrics, and the Fuzzy-Analytic Hierarchy Process (AHP) method to determine the weight of each index and ranking; and (3) determination of flood risks for the UNESCO World Heritage Sites of Hoi An City and the surrounding areas. By comparing model outputs with historical flood locations (N = 330), the study finds that >75% of past floods occurred at high or very high-risk areas forecasted by the model. Results from the model verified by true flood incidents show that the inundation hotspots are concentrated in the city's heritage sites, where human and climate changes have impacted landscape structure. The flood inundation risk mapping obtained from the synthesis analysis, encompasses hazard, exposure, and vulnerability, offering holistic knowledge to help reduce and manage flood risks at Hoi An World Heritage Sites. This method can be applicable to other sites with similar characteristics.

Sistem Informasi Geospasial Penerima Bantuan Sosial Disabilitas Menggunakan Klasterisasi Fuzzy K-Means

The data collection process for recipients of disability social assistance at Dinas Sosial has been going well, but the existing dataset is still in the form of raw data that has not been analyzed for the importance of the Dinas Sosial or Cirebon Satu data. The purpose of this study is to design a Geospatial Information System that can help to analyze, classify, and visualize spatial data from recipients of disability social assistance based on age. The methods used are fuzzy and k-means methods. Fuzzy is a method that can be used to group the age of recipients of disability social assistance and find out the degree of membership. The use of the k-means method is one method that is suitable for use in age clustering. Age will be grouped into three clusters, namely cluster 1 (Young), cluster 2 (Middle-Aged) and cluster 3 (Old). The results of this research are in the form of dataset analysis using the fuzzy k-means method in Microsoft Excel, map analysis in QGIS and maps in html format. After testing, the clustering results of fuzzy and k-means methods carried out in two iterations are "SAME". The results of map visualization are also good. The conclusion of this study is that the Geospatial Information System runs well and can be used to assist Dinas Sosial and for the importance of Cirebon Satu Data

Application of geospatial information systems (GIS) for assessment of the distribution of periodontal disease in Makassar City, South Sulawesi Province, Indonesia.

Addressing the presence of periodontal disease requires a high level of expertise to detect the disease as well as effective communication to understand patients' problems. Based on basic health data from 2018, the prevalence of this problem in Indonesia is approximately 74%. This study examined the distribution of periodontal conditions in March 2021 in Makassar City, the capital of South Sulawesi Province. To determine the distribution of periodontal disease, a questionnaire was used to find out the severity of this issue. A descriptive observational method, used with a cross-sectional design and a web-based geospatial information system (GIS) application linked to ArcGIS, was conducted. The results showed thatout of the 15 districts in Makassar City, the island district of Sangkarranghad had the highest presence of periodontal disease. Three other districts were classified as also belonging to this low category, while 11 other ones exhibited a medium disease incidence score.

Maximizing geothermal prospects: Unveiling Paraná Basin’s potential through advanced GIS and Multicriteria Decision Analysis (MCDA)

Geothermal energy is now clearly an important option for the Brazilian energy grid due to the demand for renewable energy sources. The northern and central regions of the Paraná Basin (Brazil) were identified as prospects for additional research into the region’s geothermal energy potential in this context by prior geothermal studies. A thorough investigation based on geological, geophysical, and thermal data is necessary to identify regions with a high potential for geothermal energy. When used by various authors to execute these tasks, the Multicriteria Decision Analysis (MCDA) approach built on Geospatial Information Systems (GIS) has shown considerable promise. The weights of the following parameters-rock type, fault proximity, heat flow, magnetic anomaly, and gravimetric anomaly-were determined in this study using the Analytic Hierarchy Process (AHP) approach, which was incorporated into the MCDA. Five favorability maps were created using the data pertaining to these criteria after they had been processed, standardized, and classed. From ”very low” to ”very high” levels, there were five classes for each selected parameter. From them, we created a map of the geothermal potential based on the list of criteria and the weights assigned to each. In order to verify the findings, we compared the locations of hot springs and aquifers considered indicators of geothermal activity with those of the places with higher potential. Using only one data source, the results showed that four regions had a high index of geothermal favorability, one of which had been previously identified as promising. As a result, the first map produced in the research area using the MCDA-GIS technique is shown in this paper. The strategy proposes a novel way to direct future research to deepen and thoroughly explore these four promising areas.

Mapping methane reduction potential of tidal wetland restoration in the United States

Coastal wetlands can emit excess methane in cases where they are impounded and artificially freshened by structures that impede tidal exchange. We provide a new assessment of coastal methane reduction opportunities for the contiguous United States by combining multiple publicly available map layers, reassessing greenhouse gas emissions datasets, and applying scenarios informed by geospatial information system and by surveys of coastal managers. Independent accuracy assessment indicates that coastal impoundments are under-mapped at the national level by a factor of one-half. Restorations of freshwater-impounded wetlands to brackish or saline conditions have the greatest potential climate benefit of all mapped conversion opportunities, but were rarer than other potential conversion events. At the national scale we estimate potential emissions reduction for coastal wetlands to be 0.91 Teragrams of carbon dioxide equivalents year−1, a more conservative assessment compared to previous estimates. We provide a map of 1,796 parcels with the potential for tidal re-connection.

Information model of a geoinformation system for studying the geometry of rock massif based on seismic survey data

This scientific article explores the development of an information model for a geospatial information system (GIS) to manage data on the geometry of rock formations using seismic exploration methods. The main objective of the research is to analyze the key data clusters, identify separate data repositories, and develop efficient tools for visualization and analysis of geospatial data. Additionally, the application of distributed databases for ensuring fault tolerance and improving system performance is discussed. The study examines the usage of tools such as ezdxf and ABViewer and their application for three-dimensional visualization of heterogeneous rock formations data.

Radar (sentinel 1 data)-based flood mapping in Rivers State, Nigeria

The existence of floods in Nigeria, a notable environmental issue, is primarily linked to human-induced causes. These are largely linked to global warming causing shifts in climate patterns. Flooding is not always a direct result of preceding factors like heavy rain or dam overflow. Instead, it is frequently triggered by human actions such as overloading main rivers or inappropriate land use. The purpose of this study was to pinpoint areas prone to flooding in Rivers State. The main goals included the development of a spatial map depicting areas susceptible to floods. The research leverages multiple data sources, Shuttle Radar Topography Mission Digital Elevation and Rivers State Administrative Map, as well as Sentinel-1 SAR data. Assessment of potential surface runoff and identifying low probability flood-prone areas was conducted through Change Detection method on Google Earth Engine platform. ArcGIS and QGIS played a significant role in assessing the vulnerability to flood risks. The primary analyses conducted involved overlaps analysis. The study shows a vulnerability to flooding in Rivers State across a total area of 29,660 hectares, equating to 2.75% of the state's total size. This research provides important information that can aid decision-making processes in disaster preparedness, land use planning, and the implementation of effective risk reduction measures in Rivers State, and Nigeria at large. We strongly endorse the creation of Geospatial Information Systems to record the documentation of flood zones.

Spatio-Temporal Modeling of COVID-19 Spread in Relation to Urban Land Uses: An Agent-Based Approach

This study aims to address the existing gaps in evidence regarding spatio-temporal modeling of COVID-19 spread, specifically focusing on the impact of different urban land uses in a geospatial information system framework. It employs an agent-based model at the individual level in Gorgan, northeast Iran, characterized by diverse spatial and demographic features. The interactions between human agents and their environment were considered by incorporating social activities based on different urban land uses. The proposed model was integrated with the susceptible–asymptomatic–symptomatic–on treatment–aggravated infection–recovered–dead epidemic model to better understand the disease transmission at the micro-level. The effect of various intervention scenarios, such as social distancing, complete and partial lockdowns, restriction of social gatherings, and vaccination was investigated. The model was evaluated in three modes of cases, deaths, and the spatial distribution of COVID-19. The results show that the disease was more concentrated in central areas with a high population density and dense urban land use. The proposed model predicted the distribution of disease cases and mortality for different age groups, achieving 72% and 71% accuracy, respectively. Additionally, the model was able to predict the spatial distribution of disease cases at the neighborhood level with 86% accuracy. Moreover, findings demonstrated that early implementation of control scenarios, such as social distancing and vaccination, can effectively reduce the transmission of COVID-19 spread and control the epidemic. In conclusion, the proposed model can serve as a valuable tool for health policymakers and urban planners. This spatio-temporal model not only advances our understanding of COVID-19 dynamics but also provides practical tools for addressing future pandemics and urban health challenges.

Providing an approach based on cluster functions of the Geospatial Information Systems for temporal analysis of motorcycle crashes in urban roads, case study: Rasht City

Providing an approach based on cluster functions of the Geospatial Information Systems for temporal analysis of motorcycle crashes in urban roads, case study: Rasht City

Blockchain‐based data deduplication using novel content‐defined chunking algorithm in cloud environment

AbstractThe cloud environment is inherently dynamic as users are added immensely in a short duration. It is indeed difficult to manage such user profiles and associated data. Meanwhile, the cloud data expand at a twofold‐to‐threefold rate on average, making storage space management and data integrity maintenance a mandatory task but also risky. The main approaches for addressing these data maintenance challenges in a cloud context are deduplication and data protection. In order to manage storage space, finding and removing identical copies of the same data from the cloud are possible, resulting in a reduction in the amount of storage space needed. Furthermore, duplicate copies are considerably reduced in cloud storage owing to data deduplication. Here, a decentralized ledger public blockchain network is introduced to protect the Integrity of data stored in cloud storage. This research proposes data deduplication using speedy content‐defined Chunking (SpeedyCDC) algorithm in the public blockchain. Many people and businesses outsource sensitive data to remote cloud servers because it considerably eliminates the hassle of managing software and infrastructure. However, the ownership and control rights of users data are nonetheless divided because it is outsourced to cloud storage providers (CSPs) and kept on a distant cloud. As a result, users have a great deal of difficulty in verifying the Integrity of sensitive data. Analysis using datasets from Geospatial Information Systems (GIS) revealed that the throughput increased by 5%–6% over that of the fastCDC technique, which offered Integrity since a blockchain network secured it.

Assessing barriers to participation in environmental education field trips in the Congaree Biosphere Reserve

Environmental education (EE) is instrumental in developing environmentally literate citizens with the knowledge, attitudes, and skills necessary to support sustainable development. In the case of EE school field trips in the USA, these experiences also foster learning and skill development that supports meeting state and national educational standards. However, despite the importance of EE field trip programs, not all schools participate. This study explored the barriers to accessibility to EE field trips by conducting semi-structured interviews with EE providers and administrators of middle schools in the Congaree Biosphere Reserve (CBR), South Carolina, USA. We also used geospatial information systems to investigate accessibility spatially. Results suggest that aspatial barriers such as curricular constraints, capacity to plan and lead field trips, costs, and transportation influence participation. The results also suggest that these aspatial barriers are often interlinked and that access to EE field trips is disproportionately allocated to students in private education and urban settings and that rural, poor, minority, underperforming, and English as Second Language students face additional hurdles. We discuss a range of potential solutions to overcoming these barriers that reduce participation in EE field trips in the CBR.

The Use of Augmented Reality Technologies and Georeferencing Data for the Construction of Water Supply Systems

The use of mobile devices, especially smartphones and mobile technology has increased significantly. Accordingly, various applications have been developed to meet the needs of mobile users, which include navigation. The integration of these applications with maps and various localization technologies such as augmented reality (AR) will improve the reliability of navigation systems for wayfinding under certain constraints. AR allows the user to experience an interactive environment by observing the real world augmented by digital objects. However, this technology cannot store and control data. A geospatial information system (GIS) provides storage, visualization, and analysis of geospatial data that can be used to address the challenges and shortcomings of AR. In addition, the capabilities offered by GIS can be useful for achieving sustainability goals in areas such as effective planning and resource allocation. Traditional GIS, especially two-dimensional maps, provide a weak intuitive experience for users, leading to difficulties in interpreting the map and reaching conclusions. Therefore, AR can be used as a bridge between 2D and 3D visualization. The involvement of GIS in human activities and the transition of AR use from traditional terrain outreach to industrial applications makes it worthwhile to integrate GIS with AR to benefit from the two technologies simultaneously. The simultaneous visualization of real and virtual geospatial information leads to an interactive approach. The paper analyzes the capabilities of augmented reality and geo-referenced data approaches for the construction of water systems, describes their weaknesses and strengths, notes the main characteristics of the logistic regression algorithm and considers the tasks performed by this algorithm. A comparative analysis of the algorithms is carried out.

A proposed FAIR approach for disseminating geospatial information system maps

We present a draft Minimum Information About Geospatial Information System (MIAGIS) standard for facilitating public deposition of geospatial information system (GIS) datasets that follows the FAIR (Findable, Accessible, Interoperable and Reusable) principles. The draft MIAGIS standard includes a deposition directory structure and a minimum javascript object notation (JSON) metadata formatted file that is designed to capture critical metadata describing GIS layers and maps as well as their sources of data and methods of generation. The associated miagis Python package facilitates the creation of this MIAGIS metadata file and directly supports metadata extraction from both Esri JSON and GEOJSON GIS data formats plus options for extraction from user-specified JSON formats. We also demonstrate their use in crafting two example depositions of ArcGIS generated maps. We hope this draft MIAGIS standard along with the supporting miagis Python package will assist in establishing a GIS standards group that will develop the draft into a full standard for the wider GIS community as well as a future public repository for GIS datasets.

Geospatial Analysis and Molecular Epidemiologic Study of Emerging Pulmonary Lophomoniasis in Iran: A National Registry-Based Study.

Bronchopulmonary lophomoniasis (BPL) is a protozoan pulmonary disease that has been reported sporadically, but its incidence has been increasing. However, the epidemiology and risk factors of the disease have not been clearly identified. The current study aims to identify BPL cases molecularly and assess the demographic and some environmental factors for the first time on the prevalence of BPL as a national registry-based study in Iran. Methodology. The study tested 960 patients with lower respiratory tract symptoms whose bronchoalveolar lavage samples were submitted from seven provinces of Iran to the Iranian National Registry Center for Lophomoniasis. They were tested for BPL by a newly developed polymerase chain reaction test. The study assessed the association of Normalized difference vegetation index (NDVI), digital elevation model (DEM), and geographic latitude as environmental factors and sex and age as demographic factors on the prevalence of BPL. Geospatial information systems methods and chi-squared and Pearson's correlation tests were used for the assessment of geographical and environmental factor effects and statistical analysis, respectively. Of the 960 patients, 218 (22.7%) tested positive for BPL; the highest and lowest prevalence rates were reported from the south and northeast of Iran, respectively. The study found a correlation between geographic latitude and age with BPL prevalence, but no association was found for gender, NDVI, or DEM. Most patients were over 40 years old, and the rate of disease was higher in southern latitudes. Age and geographical latitude were found to be risk factors for BPL. More exposure to dust and/or chronic pulmonary problems may explain the higher prevalence of the disease in older adults. Higher rates of BPL in lower latitudes may be due to warmer weather and longer days, which can confine individual activities indoors and result in more contact with domestic insects and infected dust.

Ecological risk identification and assessment of land remediation project based on GIS technology.

A land remediation project involves the removal of potentially toxic chemicals from a polluted site. Lands abandoned by industry are often contaminated with heavy metals like mercury, lead, chemicals, arsenic, and other toxins like dichlorodiphenyltrichloroethane biphenyls from electronic devices, and volatile organic chemicals (VOCs) from lubricants and chemicals. Risk assessment in environmental settings requires modernized systematic methodologies due to the complexity of today's environmental problems. When people eat, drink, or work in polluted environments, they put their health at risk and may even get cancer. Integrating geospatial information systems (GIS) with pollutant dispersion models makes environmental risk assessment and early warning possible. This research thus presents a GIS-based ecological risk identification and assessment model (GIS-ERIAM) for assessing risk for efficient land rehabilitation. Environmental cleanup sites' catalog information is the source of these details. With satellite imagery, GIS makes it simple to keep an eye on the environment and track the abundance of different types of plants and animals The ecological risk assessment (ERA) model can support recognition and prioritize risk management. By integrating direct and indirect environment interactions, the risk conditions of the whole ecology and its elements have been quantified and demonstrated in the study. The numerical outcomes illustrate that the recommended GIS-ERIAM model improves the performance by 98.9%, risk level prediction by 97.3%, risk classification by 96.4%, and detection of soil degradation ratio of 95.6% compared to other existing methods.