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  Subjects -> AERONAUTICS AND SPACE FLIGHT (Total: 124 journals)
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Spatial Information Research
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  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2366-3286 - ISSN (Online) 2366-3294
Published by Springer-Verlag Homepage  [2468 journals]
  • Adaptive enhancement of spatial information in adverse weather

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      Abstract: Abstract In the context of spatial information, particularly in video surveillance and intelligent transportation systems, the visibility of video images is severely impacted by adverse climates including rain, snow, and fog. Accurate and swift recognition of current weather conditions and adaptive clarification of surveillance videos are crucial to maintaining the integrity of spatial information. Addressing the limitations of traditional weather recognition methods and the scarcity of weather image datasets, a multicategory weather image block dataset was constructed. This research introduced a weather recognition algorithm that integrates image block processing with feature fusion. The algorithm uses traditional methods to extract shallow spatial features such as average gradient, contrast, saturation, and dark channel from weather images. It also employs transfer learning to fine-tune a pretrained VGG16 model, extracting deep spatial features from the model’s fully connected layers. The approach improves the SoftMax classifier’s recognition of fog, rain, snow, and clear weather photos by merging shallow and deep spatial information. This improvement is essential for the quality and reliability of spatial data in bad weather. The algorithm achieves 99.26% accuracy in weather detection; however, the best accuracy archive by state of art is 97.14%, confirming its usefulness as a module for adaptive video picture clarification in spatially informed systems.
      PubDate: 2024-02-21
       
  • Retraction Note: Comparative evaluation of attribute-enabled supervised
           classification in predicting the air quality

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      PubDate: 2024-02-12
       
  • Digital Crowdsourcing and VGI: impact on information quality and business
           intelligence

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      Abstract: Abstract This paper investigates the impact of Volunteered Geographic Information (VGI) on data quality and its implications for business intelligence. Focusing on VGI contributions within OpenStreetMap in three distinct urban settings: Tehran, London, and Los Angeles, the study finds that although a minority of users contribute the majority of data, the contributions from a broader user base are critical for integrating local knowledge. The research challenges existing methodologies in assessing VGI quality, which tend to overlook about 10% of data, often rich with local insights. This observation underscores the need for new, more inclusive assessment methods that value both regular and occasional contributions. Additionally, the study delves into the demographic and social factors influencing VGI activities, highlighting their significance in data contribution patterns. The findings are particularly relevant for urban planning, emergency response, and business sectors such as retail, logistics, and real estate, suggesting practical applications. The paper concludes by advocating for further research into comprehensive VGI quality evaluation methods, encompassing a wide range of user contributions.
      PubDate: 2024-02-01
       
  • National analysis on variations in estimates of forest cover dynamics over
           India (2001–2020) using multiple techniques and data sources

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      Abstract: Abstract This study evaluated multiple methodologies for monitoring forest and tree cover dynamics in India using remote sensing. The Forest Survey of India (FSI) biennially maps forest and tree cover, reporting areas under three crown density classes along with a pixel-level change matrix. Global Forest Watch (GFW) data use an annual Landsat time series to detect tree loss pixels from 2001 to 2020 and global data on new plantations. Cumulative forest to non-forest class transitions of 12.1 Mha was estimated by counting pixel-level using a change matrix reported by FSI and 2.43 Mha was estimated through tree loss using GFW data. However, FSI and global plantations data indicated new areas brought under tree cover/forest 14.5 Mha and 11.1 Mha, respectively. Part of these variations was due to differences in definition and methodology. This study highlights the need for mapping the regular loss and new areas under tree cover, which simple statistics of net forest cover change are unable to capture. Additionally, the locations of loss and plantations were visualized as spatial layers of a 1 × 1 km grid. Geo-located loss and gain areas would be of great interest in spatially capturing dynamics of forest biomass and carbon cycle. Enhanced greening of India reported in many studies is also supported. The nature of interventions leading to additional tree cover has also been highlighted.
      PubDate: 2024-02-01
       
  • Mapping hotspots of tuberculosis cases with validation on site in Gombak,
           Selangor, Malaysia

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      Abstract: Abstract Tuberculosis (TB) is regarded as one of the leading causes of death globally. The control strategies and resource allocation need to be prioritized in high risk areas by implementing accurate mapping of spatial heterogeneity of the disease. This cross-sectional study aimed to identify the hotspots of TB cases with validation on site in Gombak district, Malaysia. The 3325 cases of TB from 1st January 2013 to 31st December 2017 were collected from the MyTB web and Tuberculosis Information System database. The data includes individual’s ID, date of diagnosis, and patient’s address. The coordinate of each patient’s address was geocoded using Google Earth and then they were georeferenced with the base map of Gombak using geographic information system. Getis-Ord Gi* statistics was used to identify the hotspots of TB cases. The hotspots analysis were validated by capturing pictures of the locations during field visit and compared with the hotspot map. The hotspots of TB cases were consistently distributed at the southwestern part of Gombak, with 99% confidence level that includes 136 points across 5-year period, where the prison was located. Other location of hotspots includes apartments, hostels, markets, factories, and schools. The hotspots shifted gradually from the northwestern to the southwestern parts of the district. This study revealed heterogeneity in the spatial distribution of TB across vulnerable and densely populated areas, thus able to conduct early screening, initiate the treatment, and improve the intervention programme. In the future, inclusion of associated risk factors of the disease based on genotyping of the isolates was recommended to track the TB transmission from different sources to the hotspot’s location.
      PubDate: 2024-02-01
       
  • Spatial federated learning approach for the sentiment analysis of stock
           news stored on blockchain

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      Abstract: Abstract Sentiment analysis can be a useful tool in predicting stock market trends, as it allows us to gauge the overall sentiment towards a particular stock or company. When combined with news stored in a blockchain, sentiment analysis can provide a more accurate and trustworthy representation of the market. The aim of this paper is to study the spatial news and external events which disrupt the stock market movement as well as news analytics techniques to understand the impact of news by spatial sentiments on stock market movement. For the prediction of stock market trading decisions, a novel ensemble technique consisting of spatial federation of deep learning algorithms, machine learning algorithms and dictionary based approach is proposed to maintain privacy and geographic diversity. These learning techniques are federated into five groups and these groups are ranked as per the prediction accuracy of these models. A bit 1 or 0 is assigned for each federation thus creating a 5 bit pattern which can be used to predict stock market trading decision as strong buy, buy, hold, strong sell, sell.
      PubDate: 2024-02-01
       
  • Spatiotemporal analysis of air pollutants and river turbidity over
           Varanasi region, India during COVID-19 second wave

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      Abstract: Abstract World Health Organization (WHO) on 30th January 2020 declared a Public Health Emergency of International Concern during the spread of novel coronavirus in the early months of 2020 across 188 countries. The Indian government has imposed a 21-day lockdown during the first Coronavirus Virus Disease – 19 (COVID-19) wave starting from 25th March to 14th April initially. The state-wise lockdown was again imposed during the second wave (mid-march) to curb the spread. The present study focused on the effect of the lockdown during the COVID-19 s wave on the spatiotemporal variability of air pollutants in the Varanasi region, and on turbidity levels of the Ganga river using remote sensing. A decreasing trend for the selected air pollutants (NO2, SO2, CO, and HCHO), and turbidity levels were observed during the lockdown period which revealed improved air as well as water quality. The results of the present study provide robust insight into air and water quality measurements with methodological advancement in pollution susceptibility studies and can be used to achieve futuristic observations of patterns of turbidity levels. These results indicate that avoiding poor transportation planning and adopting sustainable plans across the urban agglomerations in India could reduce a significant amount of air pollution levels.
      PubDate: 2024-02-01
       
  • Comparative analysis of Air Quality Index prediction using deep learning
           algorithms

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      Abstract: Abstract This paper comprehensively reviews and compares methodologies used to monitor air quality and their impact on human health. With urbanization and industrialization increasing in emerging nations, air pollution levels have become a significant threat to human well-being. The study highlights the importance of reducing exposure to air pollution for the improvement of public health. The paper focuses on the comparative analysis of measuring the Air Quality Index (AQI) using deep learning algorithms like Long Short-Term Memory (LSTM) and classical machine learning models such as Autoregressive Integrated Moving Average (ARIMA), Decision Tree, K-Nearest Neighbour, Extreme Gradient Boosting, Gradient Boosting, Adaptive Boosting, Huber Regressor, and Dummy Regressor for AQI prediction. The performance of these models is evaluated using daily and hourly time series data from 2014 to 2018, with the Root Mean Squared Error (RMSE) used as the performance indicator. The results demonstrate that LSTM outperforms ARIMA, particularly with hourly data. For daily data, ARIMA achieved an RMSE of 97.88, whereas LSTM obtained an RMSE of 143.07. On the other hand, for hourly data, ARIMA yielded an RMSE of 69.65, while LSTM achieved a lower RMSE of 44.6539. These findings highlight the potential of deep learning algorithms, specifically LSTM, in accurately forecasting air quality.
      PubDate: 2024-02-01
       
  • Deep learning-based heat optimization techniques for forecasting indoor
           temperature changes

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      Abstract: Abstract The traditional climate compensator technique requires extensive modifications and significantly relies on manual knowledge when it comes to winter heating in buildings. This paper suggests a deep-learning-based optimization procedure for heating systems to improve the initial control strategy. To begin, we recommend using a deep MTDN (Multiple Time Difference Network) to deduce the thermodynamic laws governing the dynamics of indoor temperature changes and make future predictions about the temperature in a given space. The network is and follows physical rules; next, M is used as a simulator, and the evaluation index representing the human thermal reaction issued as the appropriate reward item. Next, a strategy optimizer based on the SAC (Soft Actor-Critic) learning thought algorithm is used to train the simulator to realize a stable and excellent heating control strategy; finally, experiments are designed using real data from a Tianjin heat exchange station to evaluate the simulator’s ability to predict future outcomes. It is confirmed that the simulator not only has high prediction accuracy but also adheres to the laws of physics and that the strategy optimizer learned approach could guarantee more stable and comfortable indoor temperature in multiple periods of random sampling, as compared to the original strategy.
      PubDate: 2024-02-01
       
  • COVID-19: adverse population sentiment and place-based associations with
           socioeconomic and demographic factors

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      Abstract: Abstract During the COVID-19 pandemic, increased adverse sentiment such as, fear, panic, anxiety was observed among the public in the United States of America (USA) apart from physical suffering and death. Authorities may find guidance for anticipation and explanation of such secondary threats by analyzing population sentiment on social media. We performed sentiment analysis (SA) using georeferenced tweets in the contiguous USA during the first two waves of COVID-19 (01 November 2019–15 September 2020). We classified the tweets into “adverse” and “non-adverse” sentiment and computed daily counts for both classes at the county-level. Utilizing clustering and Bayesian regression approaches, we analyzed the place-based demographic and socioeconomic covariates of sentiment. We detected 12 clusters that exhibited elevated adverse sentiment and discovered that higher unemployment, male population, and poverty was associated with increased odds of adverse sentiment in Tweets. Conversely, counties with higher COVID-19 case rates, rurality, and elderly population were associated with reduced odds. Pandemic preparedness, response and mitigation measures may benefit from knowledge of the geography of adverse sentiment. Combining spatial clustering and regression benefits the understanding COVID-19, as well as epidemiology in general.
      PubDate: 2024-02-01
       
  • Real time simulation of groundwater quality index using adaptive
           neuro-fuzzy inference

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      Abstract: Abstract To ensure sufficient quality and quantity of water for drinking, it is imperative to determine the contamination and quantification of potential damage of existing groundwater resources. The indexing of various water quality parameters and prediction of groundwater quality provide extensive technical assistance for the strategic management of groundwater resource. In this study, simulation of the groundwater quality index is carried out using adaptive neuro fuzzy inference system (ANFIS). The different combinations of input parameters are selected to develop the optimal model using grid partitioning and subtractive clustering FIS type. The architecture of ANFIS model is designed using Gaussian type membership function optimized through hybrid of back propagation and least square method. A total of 893 groundwater samples from 68 locations used for model development. The performance of models is weighed using correlation coefficient (R) and root mean square error. The Model 4 consisting physio0chemical and anions produces R as 0.921 and 0.837 for training and testing. The results suggested that ANFIS is a robust model that could be used with high accuracy for the prediction of groundwater quality index. Selection of adequate input parameter and ANFIS structure is a right approach for the prediction of groundwater quality and useful for decision makers for allocating water resources.
      PubDate: 2024-02-01
       
  • Mobile wireless ad-hoc network routing protocols comparison for real-time
           military application

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      Abstract: Abstract Mobile wireless Ad-hoc has become more popular because it forms quickly, has an easy setup, and has easy extensibility. The mobile ad-hoc wireless networks can be further classified according to their applications as follows: Regular user ad-hoc networks are commercial communication that applies to vehicles to help avoid collisions and accidents and live connections to transfer data from car to car. Another application is disaster rescue ad-hoc networking, usually used when a normal infrastructure network is destroyed by storms, earthquakes, tsunamis, etc. Nowadays, a lot of applications, particularly those related to the military and emergency situations, rely on mobile ad hoc wireless networks, where security needs are more challenging to provide than in regular networks. We present the tactical network needs for the military. This platform attempts to assess the possible advantages of mobile ad hoc networks in tactical military applications. This work proposes route discovery using reactive (on-demand) routing protocols where nodes need to just transfer data. This eliminates the requirement for each node to store and maintain any routing tables. This study presents and contrasts the benefits and drawbacks of two fundamental mobile ad hoc routing systems (AODV and DSR). Both protocols are On-Demand routing techniques, and when data needs to be sent, the discovery phase begins. The results of the simulation, the AODV routing approach outperforms the DSV routing method under identical simulated conditions.
      PubDate: 2024-02-01
       
  • Landslide hazard zones differentiated according to thematic weighting:
           Road alignment in North Sikkim Himalayas, India

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      Abstract: Abstract Geospatial analysis is a powerful tool for assessing landslide frequency distribution and hazard potential zones along road alignment in North Sikkim Himalayas, India. The thematic weightings used for road alignment include slope angle, slope aspect, lithology or rock types, hydrological characteristics (drainage lines), fault and thrust present, road alignment, and surface cover type. Each parameter can be assigned a numerical value based on its potential contribution towards increasing landslide susceptibility; higher values indicate greater risks while lower ones suggest less danger from these events occurring along that stretch of roadway or landform feature. Thematic weighted method has determined the weight values of various preparatory factors based on their vulnerability and estimated the landslide hazard index (LHI) by classifying the study area into four distinct hazard zones: very high (12.12%), high (40%), moderate (37.20%) and low-hazard zone (10.68%). The result reveals that 65.3% of landslides occurred in the very high-hazard zone. About 24.7% and 7.6% of the landslides were found in high and moderate-hazard zones. Only 2.4% of landslides contribute to a low-hazard zone. It has been found that the landslide frequency percentage gradually increases from low (2.4%) to very high hazard zone (65.3%). This mapping also helps planners decide where construction activities should not be performed if they are located within these hazardous zones thus helping reduce future losses due to such calamities are significant.
      PubDate: 2024-02-01
       
  • Spatio-temporal analysis of fire incidences in urban context: the case
           study of Mashhad, Iran

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      Abstract: Abstract The study aims to identify fire patterns in Mashhad, the second-most populous city in Iran, between 2015 and 2019. Spatial scan statistics were utilized to determine the spatiotemporal patterns of 29,889 fire events in the research area. There were four primary types of fires: (1) structural fires (39%), (2) vehicle fires (11%), (3) green and open space fires (19%), and (4) others (31%). The interval from 12:00 to 23:00 h was identified as the high-risk period for all fire incidents. Fires were common in the nearby city core. Additionally, three significant hourly spatial-temporal clusters of firefighting operations were identified: the western part of the city between 12:00 and 23:00, the city center between 11:00 and 22:00, and the southeastern part between 11:00 and 22:00. Population density, illiteracy ratio, unemployment ratio, youth ratio, low-income population, and the number of old buildings might be socio-economic criteria that contribute to the spatiotemporal pattern of urban fires. Urban planners might prioritize high-risk neighborhoods when allocating resources for fire safety. Future research could specifically investigate high-risk regions to identify relevant characteristics in these areas.
      PubDate: 2024-02-01
       
  • Investigating the capability of the Harmonic Analysis of Time Series
           (HANTS) algorithm in reconstructing time series images of daytime and
           nighttime land surface temperature from the MODIS sensor

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      Abstract: Abstract A continuous, high-resolution surface temperature time series is necessary for hydrology, meteorology, and ecology. However, challenges such as cloud cover, aerosols, and algorithmic disturbances in satellite-based temperature images, particularly from MODIS, result in irregular observations, data loss, noise, and spatial–temporal outliers. The effectiveness of the Harmonic Analysis of Time Series (HANTS) algorithm in reconstructed day and night temperature series from MODIS in desert regions are assessed in this study. Utilizing daily and nightly surface temperature data from 2014 to 2020 (4380 images), data gap analysis revealed peak loss during spring and winter, averaging 6.19% during the day and 8.20% at night over seven years. Because of temperature differences between day and night, the HANTS algorithm was unable to reconstruct the day-night sequence in an accurate way, highlighting the potential of the algorithm in addressing challenges associated with desert environments.
      PubDate: 2024-01-22
       
  • Direct and indirect determinants of COVID-19 outbreak in Australia: a
           spatial panel data analysis

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      Abstract: Abstract An outbreak of Coronavirus Disease 2019 (COVID-19) was experienced all around the world. Identifying the spatial prevalence of this disease can lead to more effective management and control of this outbreak. Thus, the main purpose of this paper is to evaluate the spatial effects of COVID-19 from early June 2020 to mid-August 2020, in Australia. For this purpose, the effect of hospitalized Intensive Care Unit (ICU) cases, death cases, active cases, and recovered cases on confirmed COVID-19 cases was estimated using the Spatial Durbin Model. The empirical results reveal a significant positive relationship between confirmed COVID-19 cases and death cases, active cases, and ICU hospitalizations. Also, the recovered cases have a significant negative effect on confirmed COVID-19 cases. In addition, hospitalized ICU cases have the biggest effect on confirmed COVID-19 cases in the short and long run. These results can help healthcare providers in managing the demand for healthcare services throughout the country. Moreover, government officials and policymakers can use the findings of this study in the effective application of quarantine practices as well as resource allocation in different states during this pandemic.
      PubDate: 2024-01-22
       
  • Examining the effect of apartment attributes on their sale prices in
           Riyadh, Saudi Arabia

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      Abstract: Abstract Hedonic regression is used to investigate the influence of various attributes on the apartment sale prices in Riyadh, Saudi Arabia. 592 apartments were sampled from online sale advertisements during early 2019. Relevant internal attributes such as apartment age and floor area were recorded during the sampling process. Apartments were then mapped using ArcGIS to account for apartments’ external attributes such as proximity to commercial centers and metro stations. Adopting a semi-log regression model (where the dependent variable, sale price, is log transformed) provided more explanatory power than regular linear regression. Internal attributes that had a positive relationship with the price were floor area, furnishings, the availability of yards and elevators, while attributes such as building age, the floor level of the apartment influenced prices negatively. As for external attributes, proximity to expressways, colleges, hospitals and future metro stations influenced prices positively, while the effect of proximity to hypermarkets and malls was positive only in the linear hedonic model. Among three probable Central Business Districts (CBDs) in Riyadh, Olaya district had the strongest positive influence on apartment prices. Proximity to the King Abdullah Financial District (KAFD) was positively associated with prices. However, contrary to what’s expected, proximity to the downtown (the traditional and formally recognized city center of Riyadh) negatively influenced the prices.
      PubDate: 2024-01-02
       
  • Correction: Assessing the impact of spatio-temporal land use and land
           cover changes on land surface temperature, with a major emphasis on mining
           activities in the state of Chhattisgarh, India

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      PubDate: 2023-12-18
       
  • Decoding spatial precipitation patterns using artificial intelligence

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      Abstract: Abstract The primary objective of this research is to examine the spatio-temporal variations in precipitation within the Indian Monsoon region (IMR), with a particular focus on regional relationships. This investigation utilizes rainfall data collected from rain gauge stations for the year 2020, a year marked by extreme weather events such as floods in Assam and Mumbai, Cyclone Amphan, and multiple cloud-bursts in the Western Himalayan regions. The time series analysis is conducted to find the precipitation patterns across six distinct geographical zones. A numerical association rule is formulated by leveraging both k-means clustering and Apriori techniques. The central finding of this study underscores the North Eastern region’s prominent co-occurrence pattern of rainfall events, particularly concerning lead days. Specifically, when there is rainfall in the preceding days, there is a notable likelihood of continued rainfall on the subsequent day. This prolonged and consecutive rainfall pattern, persisting for three successive days, emerges as a one of the major contributing factors to the flooding incidents experienced in these regions.
      PubDate: 2023-12-13
       
  • How does ChatGPT evaluate the value of spatial information in the 4th
           industrial revolution'

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      Abstract: Abstract Chat Generative Pre-trained Transformer (ChatGPT), developed by OpenAI, is a prominent AI model capable of understanding and generating human-like text based on input. Since terms and concepts of spatial information are contextual, the applications of ChatGPT on spatial information disciplines can be biased by the perceptions and perspectives of ChatGPT towards spatial information. Therefore, a thorough understanding of the real magnitude and level of comprehension of spatial information by ChatGPT is essential before exploring its potential applications in spatial information disciplines. This article aims to investigate how ChatGPT evaluates spatial information and its potential contributions to 4th Industrial Revolution (Industry 4.0). ChatGPT has summarized a notable perspective on evaluating and utilizing spatial information in the context of the Industry 4.0. The result of this study shows that ChatGPT has a good understanding on contextual concepts related to spatial information. However, it exhibits potential biases and challenges, as its responses lean towards the technological and analytical aspects. The results provide a crucial understanding on how to leverage ChatGPT’s benefits to the fullest while recognizing its constraints, with the aim to enhance the efficacy from the perspective of applications linked to spatial information.
      PubDate: 2023-12-12
       
 
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  Subjects -> AERONAUTICS AND SPACE FLIGHT (Total: 124 journals)
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