Geospatial Data Analysis Research Articles

Multi-parameter study of Aculeo lagoon (Chile)

Abstract. The Aculeo Lagoon, located in the Santiago Metropolitan Region, was one of the main water reservoirs of central Chile. It has experienced a decline in water levels since 2009 and completely dried up in 2018. It was unable to recover until 2023 due to sustained demand for water resources associated with population growth, changes in land use, and years of drought. The objective of this study is to estimate how anthropogenic factors and climate change have influenced the drying of the lagoon. Parameters considered include population growth, housing increases, changes in land use within the Aculeo basin, local temperature and precipitation variations, and global El Niño Southern Oscillation (ENSO) phenomena. Input data included multi-year Landsat images, census data, and a systematic review of the literature on the study area. Furthermore, applying geospatial data analysis techniques such as satellite image processing, supervised classification of land cover/use, and calculation of spectral indices, it is possible to conclude that the Aculeo Lagoon dried out due to a significant precipitation deficit over nearly a decade, exacerbated by the overexploitation of land for agricultural activities.

Unleashing the Dynamic Strategies in the Era of Change – A Spotlight on the ISPRS Student Consortium's Global Contributions

Abstract. ISPRS Student Consortium (ISPRS SC) has played a significant role in bringing students and young researchers closer to the field of remote sensing and photogrammetry in the evolving landscape of geospatial sciences. Innovative strategies and events have been employed by the ISPRS SC to address contemporary challenges and harness emerging opportunities in the field of photogrammetry and remote sensing. Through an analysis of the global contributions of ISPRS SC, ranging from research initiatives to the online webinar series, this paper underscores the consortium's commitment to fostering collaboration, innovation, and knowledge exchange among young scholars and professionals worldwide. The paper highlights key thematic areas addressed by ISPRS SC such as advancements in remote sensing technologies, geospatial data analysis, and applications in diverse domains including environmental monitoring, agriculture, urban planning, and disaster management. Moreover, in this paper, we emphasize the role of the consortium in nurturing future leaders in geospatial sciences through ISPRS SC mentorship programs, networking opportunities, and capacity-building initiatives. By highlighting the initiatives led by the ISPRS Student Consortium, this paper underscores the key role of youth-driven innovation for positive change and shaping the future of geospatial sciences on a global scale.

Exploiting Legal Reserve Compensation as a Mechanism for Unlawful Deforestation in the Brazilian Cerrado Biome, 2012–2022

This research investigates the illegal Vegetation Suppression Authorization (VSA) for Legal Reserve Compensation (LRC) in Tocantins, Brazil, between 2012 and 2014. The VSA is a license issued by state environmental agencies that authorizes the removal or clearing of native vegetation. This infraction resulted from the state environmental agency’s mistaken interpretation of Resolution 07/2005 of the State Environmental Council and the Native Vegetation Protection Law, which operates nationally and takes precedence over state resolutions and laws. The study area focuses on eight municipalities contained in the Formoso, Pium, and Javaés river basins. The methodology examines irregular VSA-LRC cases through geospatial data analysis, multitemporal analysis using remote sensing data, and economic assessments in both the origin and destination areas of the compensatory reserve. The results showed that among the 217 VSA-LRC applications during the period, 93 had corresponding geospatial data, indicating that 57.1% of the geospatial data were missing. This discrepancy between the authorized area and the available geospatial information restricts the oversight capability of the regulatory agency. A decade-long deforestation analysis of VSA-LRCs using remote sensing data considered three phases: 2012–2014 (application and issuance of VSA-LRCs), 2015–2018 (operational issuance), and 2019–2022 (post-expiration of VSA-LRCs). The results revealed a total VSA-LRC area of 343.291 km2 (34,329.10 ha), with 229.1169 km2 (22,911.69 ha) of deforestation. Notably, 54.877 km2 (5487.70 ha) of this deforestation occurred in 2019–2022, after the VSA-LRC had expired. The analysis of the LRC origin and destination areas demonstrates a unidirectional strategy, from economically more valuable areas to less valuable ones, according to land market dynamics. Therefore, this investigation concludes the need for greater administrative transparency, the development of a reliable geospatial data system for monitoring, and policy changes and actions to enhance environmental protection.

A place-based spatial analysis of racial inequities in overdose in St. Louis County Missouri, United States

ObjectiveThe objective of this study was to identify place features associated with increased risk of drug-involved fatalities and generate a composite score measuring risk based on the combined effects of features of the built environment. MethodsWe conducted a geospatial analysis of overdose data from 2022 to 2023 provided by the St. Louis County Medical Examiner's Office to test whether drug-involved deaths were more likely to occur near 54 different place features using Risk Terrain Modeling (RTM). RTM was used to identify features of the built environment that create settings of heightened overdose risk. Risk was estimated using Relative Risk Values (RRVs) and a composite score measuring Relative Risk Scores (RRS) across the county was produced for drugs, opioids, and stimulants, as well as by Black and White decedents. ResultsIn the model including all drugs, deaths were more likely to occur in close proximity to hotels/motels (RRV=39.65, SE=0.34, t-value=10.81 p<.001), foreclosures (RRV=4.42, SE=0.12, t-value = 12.80, p<.001), police departments (RRV=3.13, SE=0.24, t-score=4.86, p<.001), and restaurants (RRV=2.33, SE=0.12, t-value=7.16, p<.001). For Black decedents, deaths were more likely to occur near foreclosures (RRV=9.01, SE=0.18, t-value =11.92, p<.001), and places of worship (RRV= 2.51, SE=0.18, t-value = 11.92, p<.001). For White decedents, deaths were more likely to occur in close proximity to hotels/motels (RRV=38.97, SE=0.39, t-value=9.30, p<.001) foreclosures (RRV=2.57, SE=0.16, t-value =5.84, p<.001), restaurants (RRV=2.52, SE=0.17, t-value=5.33, p<.001) and, auto painting/repair shops (RRV=0.04, SE=0.18, t-value =3.39, p<.001). ConclusionThese findings suggest that places of worship, the hospitality industry, and housing authorities may be physical features of the environment that reflect social conditions that are conducive to overdose. The scaling up of harm reduction strategies could be enhanced by targeting places where features are co-located.

A triadic model of job retention and turnover dynamics in the hospitality industry

Purpose This study aims to examine key factors influencing hospitality employee turnover in a post-pandemic context, challenging conventional assumptions about the role of demographics and work-related factors in retention decisions. Design/methodology/approach The research adopted a comprehensive framework using the capability approach and geospatial data analysis, integrating social vulnerability indexes with survey responses from 797 hospitality employees. This study analyzed turnover intentions across demographics, job roles and regions, focusing on Florida’s I-4 corridor. Findings Individual conversion factors like age and marital status were the strongest predictors of turnover, with older and married employees less likely to leave their jobs. In contrast, traditional variables like income, education and job type did not significantly influence turnover intentions. The pandemic blurred distinctions between job roles, highlighting personal circumstances, health risks and economic pressures as critical factors. Contrary to expectations, financial assistance did not significantly reduce turnover intentions. In addition, employees’ life circumstances, such as social vulnerability, influence labor relations and turnover more than work-related conditions. Research limitations/implications This study enriches turnover literature by confirming that personal life circumstances, like age and marital status, are pivotal in understanding employee retention. It challenges conventional demographic and work-related predictors and urges future research to explore the interaction between personal and professional factors in the hospitality industry. The study’s agent-based framework deepens the understanding of how various factors shape employee decisions to stay or leave. Practical implications The findings suggest that hospitality employers must develop more localized, employee-centric retention strategies, especially for younger employees. Tailored approaches considering regional and demographic differences, such as providing career development opportunities and flexible work conditions, could foster long-term loyalty. In rural areas, retention efforts should focus on improving job satisfaction and community support, while in urban areas, strategies prioritize career progression and flexibility. Social implications The study’s findings have significant social implications, particularly in reshaping how employee retention strategies are developed in the hospitality industry. By emphasizing life circumstances over traditional work-related factors, the research highlights the importance of supporting employees’ resilience, especially for those facing social vulnerability. Employers may need to create more flexible and inclusive policies that account for personal, economic and health-related challenges. In addition, the findings suggest that financial aid alone is insufficient in reducing turnover, calling for a more holistic approach that integrates emotional and social support to foster a more stable and loyal workforce. Originality/value This study challenges traditional turnover models by shifting focus from work-related factors to life circumstances, particularly the resilience of older and married employees. It integrates three dimensions – personal (sociodemographics), social (support) and environmental (job type, pandemic challenges) – to examine their influence on employee agency. This triadic framework offers insights into how individual, social and contextual factors shape turnover decisions.

Geospatial tools and data for health service delivery: opportunities and challenges across the disaster management cycle.

As extreme weather events increase in frequency and intensity, the health system faces significant challenges, not only from shifting patterns of climate-sensitive diseases but also from disruptions to healthcare infrastructure, supply chains and the physical systems essential for delivering care. This necessitates the strategic use of geospatial tools to guide the delivery of healthcare services and make evidence-informed priorities, especially in contexts with scarce human and financial resources. In this article, we highlight several published papers that have been used throughout the phases of the disaster management cycle in relation to health service delivery. We complement the findings from these publications with a rapid scoping review to present the body of knowledge for using spatial methods for health service delivery in the context of disasters. The main aim of this article is to demonstrate the benefits and discuss the challenges associated with the use of geospatial methods throughout the disaster management cycle. Our scoping review identified 48 articles employing geospatial techniques in the disaster management cycle. Most of them focused on geospatial tools employed for preparedness, anticipatory action and mitigation, particularly for targeted health service delivery. We note that while geospatial data analytics are effectively deployed throughout the different phases of disaster management, important challenges remain, such as ensuring timely availability of geospatial data during disasters, developing standardized and structured data formats, securing pre-disaster data for disaster preparedness, addressing gaps in health incidence data, reducing underreporting of cases and overcoming limitations in spatial and temporal coverage and granularity. Overall, existing and novel geospatial methods can bridge specific evidence gaps in all phases of the disaster management cycle. Improvement and 'operationalization' of these methods can provide opportunities for more evidence-informed decision making in responding to health crises during climate change.

Exploring Topological Information Beyond Persistent Homology to Detect Geospatial Objects

Accurate detection of geospatial objects, particularly landslides, is a critical challenge in geospatial data analysis due to the complex nature of the data and the significant consequences of these events. This paper introduces an innovative topological knowledge-based (Topological KB) method that leverages the integration of topological, geometrical, and contextual information to enhance the precision of landslide detection. Topology, a fundamental branch of mathematics, explores the properties of space that are preserved under continuous transformations and focuses on the qualitative aspects of space, studying features like connectivity and exitance of loops/holes. We employed persistent homology (PH) to derive candidate polygons and applied three distinct strategies for landslide detection: without any filters, with geometrical and contextual filters, and a combination of topological with geometrical and contextual filters. Our method was rigorously tested across five different study areas. The experimental results revealed that geometrical and contextual filters significantly improved detection accuracy, with the highest F1 scores achieved when employing these filters on candidate polygons derived from PH. Contrary to our initial hypothesis, the addition of topological information to the detection process did not yield a notable increase in accuracy, suggesting that the initial topological features extracted through PH suffices for accurate landslide characterization. This study advances the field of geospatial object detection by demonstrating the effectiveness of combining geometrical and contextual information and provides a robust framework for accurately mapping landslide susceptibility.

Rekonstrukcja miejsca zdarzenia drogowego w oparciu o dane geoprzestrzenne Geoportalu Krajowego

The purpose of the article is to analyze the potential use of free spatial data available in the National Geoportal to support traffic accident reconstruction. The research focuses on the capabilities and limitations of using LiDAR and orthophotomaps data in the context of reconstructing traffic accident scenes. A comparative methodology was employed, involving the analysis of geospatial data in relation to traditional surveying techniques, exemplified by a real traffic accident analysis using the PC-Crash soft-ware. The main findings of the research indicate high precision of orthophotos and LiDAR data in reproducing the actual topography and elements of road infrastructure. The analysis conducted on a 3D model created from LiDAR data showed that modern measurement techniques allow a more accurate reconstruction of the accident's trajectory and assessment of road visibility conditions. Integrating these data with traditional measurement methods can significantly enhance the standards of forensic experts' work, contributing to an increased accuracy in determining accident scenarios and, more broadly, to a better understanding of accident causes.

HierGP: Hierarchical Grid Partitioning for Scalable Geospatial Data Analytics

Application domains such as environmental health science, climate science, and geosciences—where the relationship between humans and the environment is studied—are constantly evolving and require innovative approaches in geospatial data analysis. Recent technological advancements have led to the proliferation of high-granularity geospatial data, enabling such domains but posing major challenges in managing vast datasets that have high spatiotemporal similarities. We introduce the Hierarchical Grid Partitioning (HierGP) framework to address this issue. Unlike conventional discrete global grid systems, HierGP dynamically adapts to the data’s inherent characteristics. At the core of our framework is the Map Point Reduction (MPR) algorithm, designed to aggregate and then collapse data points based on user-defined similarity criteria. This effectively reduces data volume while preserving essential information. The reduction process is particularly effective in handling environmental data from extensive geographical regions. We structure the data into a multilevel hierarchy from which a reduced representative dataset can be extracted. We compare the performance of HierGP against several state-of-the-art geospatial indexing algorithms and demonstrate that HierGP outperforms the existing approaches in terms of runtime, memory footprint, and scalability. We illustrate the benefits of the HierGP approach using two representative applications: analysis of over 289 million location samples from a registry of participants and efficient extraction of environmental data from large polygons. While the application demonstration in this work has focused on environmental health, the methodology of the HierGP framework can be extended to explore diverse geospatial analytics domains.

Performance of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) Model in the Northeast Region of Brazil

Study is presented on the potential for using the tools contained in the Google Earth Engine (GEE) geospatial data processing and analysis platform in climatological studies in the Northeast Region of Brazil (NEB), in particular the validation of estimates of total annual rainfall obtained by the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) model in seven homogeneous regions of the NEB. This data is made up of various sources of meteorological information, including information from satellites, numerical models and observations at weather stations in operational and research centers across the globe. The performance of the CHIRPS model was assessed using climatological data obtained from the National Meteorological Institute's (INMET) meteorological database, metrics and statistical tests. The results showed that the CHIRPS model has potential for use in six of the seven regions studied, with low errors and strong statistical correlation, and limitations in one of these regions. The conclusion is that this technological tool, which has a low operating cost and is free of charge, can be used to assist in climatological studies, planning and the development of strategies for mitigation, adaptation and coexistence with the dry periods observed in the sub-regions of the NEB, especially in regions that are in a situation of socio-economic vulnerability, as is the case in the semi-arid region of the NEB.

Exploring Cesarean Section Delivery Patterns in South India: A Bayesian Multilevel and Geospatial analysis of Population-Based Cross-Sectional Data

BackgroundThis paper focuses on the period from 2019 to 2021 and investigates the factors associated with the high prevalence of C-section deliveries in South India. We also examine the nuanced patterns, socio-demographic associations, and spatial dynamics underlying C-section choices in this region. A cross-sectional study was conducted using large nationally representative survey data.MethodsNational Family Health Survey data (NFHS) from 2019 to 2021 have been used for the analysis. Bayesian Multilevel and Geospatial Analysis have been used as statistical methods.ResultsOur analysis reveals significant regional disparities in C-section utilization, indicating potential gaps in healthcare access and socio-economic influences. Maternal age at childbirth, educational attainment, healthcare facility type size of child at birth and ever pregnancy termination are identified as key determinants of method of C-section decisions. Wealth index and urban residence also play pivotal roles, reflecting financial considerations and access to healthcare resources. Bayesian multilevel analysis highlights the need for tailored interventions that consider individual household, primary sampling unit (PSU) and district-level factors. Additionally, spatial analysis identifies regions with varying C-section rates, allowing policymakers to develop targeted strategies to optimize maternal and neonatal health outcomes and address healthcare disparities. Spatial autocorrelation and hotspot analysis further elucidate localized influences and clustering patterns.ConclusionIn conclusion, this research underscores the complexity of C-section choices and calls for evidence-based policies and interventions that promote equitable access to quality maternal care in South India. Stakeholders must recognize the multifaceted nature of healthcare decisions and work collaboratively to ensure more balanced and effective healthcare practices in the region.

Remote sensing insights into land cover dynamics and socio-economic Drivers: The case of Mtendeli refugee camp, Tanzania (2016–2022)

The purpose of this article is to present the scope and the dynamics of the environmental changes unfolded in the vicinity of Mtendeli refugee camp. It presents a new method, which combines geospatial analysis of high-resolution Earth observation data (Sentinel-1&2) with ground-based observations and input from local experts. Time series classifications of annual land use/land cover in the surroundings of the camp is developed from remote data. Subsequently main transitions and trends are quantitatively achieved. This is a first study which, not only treats the land transition process in a comprehensive manner, but also tracks the changes and their main drivers on an annual scale over the lifetime of the camp (2016–2021) and the post-closure situation in 2022. Most importantly, thanks to the involvement of social studies, it unfolds the socio-economical drivers of those changes. Drawing upon a random forest algorithm and available databases, we achieve overall classification accuracies of 83.5% (2020) and 82.0% (2022). Our findings indicate an ongoing expansion of cropland between 2016 and 2021, to the detriment of natural vegetation classes. The impact of environmental restoration programs implemented in the former camp area is visible by 2022. The proposed method can be used to identify areas of environmental risk and thus support decisions linked with sustainable development and land management.

Flood Exposure, Vulnerability, and Risk Distribution in Urban Areas: Application of Geospatial Data Analytics and Index Development

Over the past few decades, cities have experienced increased floods affecting property and threatening human life as a result of a warming planet. There is still an incomplete understanding of the flood risk patterns in urban communities with different socioeconomic characteristics. In this study, we produced separate flood exposure and vulnerability indices based on relevant factors, then combined them as a risk index for Houston, Texas and Charleston, West Virginia. We applied statistical methods to extract the most significant social vulnerability factors in each study area. Finally, we mapped significant hot spots or clusters of high flood risk and compared results to socioeconomically disadvantaged populations. Based on the results, high-risk or 1%-annual-chance floodplains cover 23% of the Houston and 7% of Charleston study areas. Within these floodplains, 13% of the total developed land in Houston and 9% in Charleston are situated. In the event of a 1%-annual-chance flood, an estimated 5% of the total population in Houston and 6% in Charleston may require evacuation. Statistically significant flood risk clusters could only be identified in Houston. The implications from this work help to provide an analysis framework for larger urban areas while offering suggestions for its improvement in smaller populated areas.

Innovations in Geospatial Data Analysis: Applied Nonlinear Analysis, Remote Sensing, AI, and GIS for Environmental Sustainability

Innovations in Geospatial Data Analysis: Applied Nonlinear Analysis, Remote Sensing, AI, and GIS for Environmental Sustainability

Landslide disaster risk analysis in Kebomas District, Gresik District

Background: Landslides occurred during the rainy season in Kebomas District, Gresik Regency, which has topography in the form of hills left over from limestone mining in 1960-1990. Field surveys conducted from December 2021 to March 2022 stated that landslides occurred during the rainy season in Kebomas District and caused damage to surrounding settlements. Slope, rainfall, rock type, soil type, and land use are factors that influence the occurrence of landslides. The purpose of this study was to map the level of landslide vulnerability in Kebomas District using a Geographic Information System. Method: The methodologies used in this study include the application of GIS for geospatial data analysis, assessment of landslide hazard potential through secondary data and field surveys, and vulnerability analysis of physical data. Additionally, a qualitative analysis of landslide disaster risk was conducted, combining hazard and vulnerability factors. These methods aim to identify and prioritize landslide-prone areas in Gresik Regency for better disaster risk management. Findings: The results showed that there were three levels of landslide vulnerability in Kebomas District, namely low, medium and high. The low vulnerability class is found in all villages in the Kebomas District, most of which are in the six villages or sub-districts in the south, west and north, namely; Dahanrejo, Kedanyang, Prambang, Kembangan, Randuagung, and Segoromadu. The moderate vulnerability class is found in all villages in the Kebomas District, most of which are in the six villages in the southeast, northwest and north, namely; Randuagung, Prambang, Dahanrejo, Kembangan, Sidomoro, and Ngargosari. Areas with a high vulnerability class are located in the central part of the sub-district which includes 15 villages or sub-districts, most of which are in four villages, namely; Klangonan, Kawisanyar, Randuagung, and Sidomoro. Conclusion: This study shows that mapping the level of landslide vulnerability in the Kebomas District can help plan and manage disaster risks more effectively, especially in areas with high vulnerability. Novelty/Originality of this article: The results of this study provide a basis for creating more targeted landslide mitigation and handling strategies, as well as the need for special attention in areas with moderate to high vulnerability.

Identifying the Authenticity of Plantscapes through Classics: A Case Study of Beijing Suburbs in the Qing Dynasty

The plantscapes surrounding historical gardens hold significant value, reflecting the natural pristine state as well as demonstrating the cultural attributes of the landscape. This study aims to develop a method for identifying the characteristics of historic plantscapes and to recognize the authenticity of historic landscapes from the perspective of plant elements. Our method combines textual and geospatial data analysis to examine the plant species, their relationships and combinations, and spatial distribution. The case study focuses on the Beijing suburbs during the Qing Dynasty, as documented in A Collection of Past Events in Beijing. We identified 658 plants recorded, encompassing 44 families and 58 genera. These plants were categorized into 7 groups based on the growth type and morphological characteristics, leading to 54 plant relationship outcomes, 107 plant combination scenarios, 5 plant combination categories, and 7 representative plant combinations. Additionally, we mapped the spatial distribution of plants, forming 16 plantscape groups and depicting the spatial kernel density distribution of important plants. We also determined the characteristics of plantscapes in different directions in the suburb. Our findings advocate for respecting the historical development of the plantscape and understanding its evolution, particularly emphasizing the use of high-quality native plants and plant combinations.

Análise sobre o uso da terra no município de Gandu, Baixo Sul Baiano: Interação da cultura cacaueira com o bioma de Mata Atlântica

The present research aims to analyze the land use in the municipality of Gandu, in the Southern Bahian Lowlands, and the interaction of the cocoa crop with the Atlantic Forest biome. As a methodology, theoretical and empirical surveys were carried out on phytophysiognomic diversity, land use and some species of the Atlantic Forest biome, as well as their agroecological relationship with cocoa crop. Interviews were made and questionnaires were applied on the relationship between cocoa production and the forest, field visits to cocoa farms, photographs of the forest (camera), cocoa cultivation in the municipality and also the identification of species with greater incidence in the landscape, use of the GIS (Geographic Information System) system as a geographic database for the analysis of geospatial data and generation of a map of location and land use. The results obtained reveal that most of the interviewees say that there has been degradation in the vegetation cover of the biome in recent decades, the vast majority say that the preservation of the forest is associated with the cultivation of cocoa, and that without it, the devastation of the forest was greater for the implementation of other crops such as cattle raising, bananas and subsistence crops at the time of the witches' broom. The fieldwork, together with the satellite images, shows that the forest is quite altered and this is associated with the use of the land: called capoeirinha, capoeira and capoeirão and more or less preserved fragments of the native ombrophilous forest in mountainous areas. It is concluded that the crop relationship is the most recurrent and oldest land use practice in the municipality of Gandu-Ba and that even with the changes in the dense ombrophilous forest, the cocoa crop contributes to the conservation of the Atlantic Forest biome through its Agroforestry System (AFS) that conserves part of the forest for exotic shading.

Modelling Christianisation: A Geospatial Analysis of the Archaeological Data on the Rural Church Network of Hungary in the 11th–12th Centuries. By Mária Vargha.

Modelling Christianisation: A Geospatial Analysis of the Archaeological Data on the Rural Church Network of Hungary in the 11th–12th Centuries. By Mária Vargha.

A comprehensive assessment of the importance of the “Maritime Silk Road” route based on multi-dimensional data-driven approaches

Worldwide straits are crucial pathways for global trade and energy transportation, occupying key strategic positions. Therefore, this study utilizes automatic identification system (AIS) data and the structure of the maritime transportation network formed by ship navigation, combined with geospatial big data analysis, to fully consider factors including the natural navigational conditions of routes, the support capabilities of surrounding ports, the geographic advantages of neighboring countries, and the distribution of the shipping network importance in the CCAS evaluation model. To assess the navigability of maritime routes, a data-driven model was constructed using three layers of this model. Empirical analyses were conducted on 22 major routes along China's coast and the “Maritime Silk Road”. This method has significantly enhanced the accuracy and reliability of route assessments, and the improved assessment model demonstrated better navigational conditions in the South China Sea routes, identifying internal factors hindering the development of these critical maritime routes. The findings of this study provide a comprehensive assessment and analysis framework for global maritime transportation, offering crucial reference information for key stakeholders, including policy makers, port and berth infrastructure investors, maritime economists, and large shipping companies. Additionally, the study's results can be practically applied in the actual shipping trade, emphasizing the role of our framework in facilitating effective decision-making and strategic planning within the maritime industry.

Trends and relationships between airport operations and noise complaints in Maryland between 2016 and 2023

The noise pollution from aircraft near airports significantly affects the health and quality of life of the local community either near or far away. This study analyzes trends in airport noise complaints in Maryland from 2016-2018. Data on noise complaints were extracted from airport noise quarterly reports and the data included the number of complaints and unique complaints from different communities near airports, as well as average aircraft operations. The number of complaints rose from less than 1,000 in 2016 to nearly 10,000 in 2018. This study aims to identify the communities most impacted by increasing airport noise through descriptive and geospatial analysis of complaint data. Results will highlight high-complaint areas to inform future noise mitigation policies and airport expansions in Maryland. The number of noise complaints increased drastically from 2016-2018, while flight operations remained steady. Correlational analyses examine the strength of relationship between flight volume and complaint volume over time. Boeing 737-700 operations has the most significant relationship with the number of unique complainants around Baltimore/Washington International Airport. Results will help inform policies to mitigate noise impacts by identifying whether reducing operations, changing flight paths, or other strategies could effectively reduce complaints as well as help the meeting the noise reduction design goals of the state.