 Subjects -> AERONAUTICS AND SPACE FLIGHT (Total: 124 journals)
| A B C D E F G H I J K L M N O P Q R S T U V W X Y Z | | The end of the list has been reached or no journals were found for your choice. |
|
|
- Non-uniform effect of COVID-19 lockdown on the air quality in different
local climate zones of the urban region of Kochi, India-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Deterioration of ambient air quality associated with urbanisation is a serious concern in many parts of the world. In India, air pollution, primarily due to particulate matter, has increased exponentially in the last few decades due to rapid urbanization, industrialization and population growth. This study investigates the non-uniform influence of COVID-19 lockdown on the ambient air quality of three distinct local climate zones (LCZs) within the urban region of Kochi (Kerala, India). The analysis of the air pollutant data of the ambient air quality monitoring stations during the pre-lockdown (PRLD), lockdown (LD) and post-lockdown (PTLD) periods of 2021 implies the significance of lockdown measures in the improvement of urban air quality. The air quality index (AQI) exhibits characteristic variability in different LCZs and contrasting behaviour between the LD period of 2020 and 2021, primarily due to the differences in the lockdown restrictions and strategies as well as the influence of local climatic factors. This study highlights the need for multiple monitoring stations in the urban regions with respect to different LCZs to identify the urban air quality hot spots.
PubDate: 2023-04-01
- Is eThekwini metropolitan municipality (EMM) experiencing light
pollution': A remote sensing analysis of nighttime data of EMM, South
Africa-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Nighttime light remote (NTL) sensing has improved the approaches taken in studying natural and social sciences. NTLs on the Earth surface can be utilised to study huma-related studies such as urbanisation, population, and light pollution. In most cases, NTL application studies are common in fast developing and developed countries such as China and United State of America while African countries are still left behind in taking part in these studies. The study aimed at assessing the effectiveness of NTL data and the extent of light pollution in urban areas of South Africa. This study explores the use of NTL data in studying light pollution in eThekwini Metropolitan Municipality, South Africa. The study quantified and assessed light pollution and its sources in the Municipality using spatial analyst tools such as reclassification and supervised images classification (Maximum likelihood) algorithm. The classes of light pollution were classified ranging from very low to very high light pollution. With land use land cover (LULC) classes representing main sources of light pollution. The study discovered that light pollution is mainly found in and around the city centre of the municipality where main economic and human activities take place. This is where mainly the built-up and commercial LULC classes were observed to be located. The correlation analysis between light pollution and LULC classes revealed strong correlation between high and very high light pollution classes and these LULC classes. These are the areas making use of artificial light at night leading to light pollution in the Municipality.
PubDate: 2023-04-01
- Spatio-temporal growth of a traditional urban centre in Nigeria
-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract This study analysed the spatio-temporal land use and land cover (LULC) changes in Ijebu Ode, Nigeria; determined the changes in the urban land use between 1986 and 2021; and predicted the future LULC changes in the study area. The study used data obtained through Global Positioning System (GPS) receiver and satellite imageries of 1986, 2000, 2014 and 2021. Data were analysed using digital image processing change detection techniques and CA-Markov analysis using TerrSet Geospatial Modelling and Monitoring System, version 19.0 and ArcMap 10.8.1 software. Results indicated that the built-up area of Ijebu Ode expanded from 9.073 km2 in 1986 to 36.43 km2 in 2021 due to reduction in bare land, cultivated area and vegetation. Results of transition probability indicated that while vegetation and built-up areas incessantly increasing, bare land and cultivated land were characterised with continual dynamisms. Result of land use simulation showed that by 2060 the built-up area would have increased to 47.32 km2. The study noted a situation of land use invasion and succession through transition of other land uses to built-up. The study concluded that the expansion of the built-up area is a signal towards urban sprawl in the study area. Hence, the study recommended that for a constant checking and evaluation of physical planning implication of land uses.
PubDate: 2023-04-01
- Spatial data analysis of Mahatma Gandhi national rural employment
guarantee scheme and its influencing factors-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract As a part of the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) program in India, durable assets under various work categories are created in rural areas by employing adult members belonging to the marginal communities. Taking the Prakasam district in Andhra Pradesh as an example, we analyzed the spatial clustering of the various work implemented under MGNREGA using kernel density estimation (KDE). We also analyzed the spatial clustering of villages in terms of overall assets and their statistical significance using hotspot analysis (Getis-Ord). The socio-economic factors influencing village-level clustering are analyzed using a machine learning model. Results show significant spatial variations in kernel density and village hotspots, thus, indicating the demand-driven nature of the program as influenced mainly by the marginal worker population female followed by the main other worker population. The study thus offers a methodological framework that may help improve and complement the empirical indicators currently employed in mapping the performance of the MGNREGA and similar programs.
PubDate: 2023-04-01
- Prediction of phishing websites using machine learning
-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract With the growing popularity of the information science, more application is being integrated with websites that can be accessed directly through the internet. This has increased the possibility of attack by ill-legal persons to steal personal information. To identify a phishing assault, several strategies have been presented. However, there is still opportunity for progress in the fight against phishing. The objective of this research paper is to develop a more accurate prediction model using Decision Tree (DT), Random Forest (RF) and Gradient Boosting Classifiers (GBC) with three features selection techniques Extra Tree (ET), Chi-Square and Recursive Feature Elimination (RFE). Since phishing websites dataset contains 89 features, therefore we have applied extra tree and chi-square, feature selection method to identify the limited important features and then recursive features elimination technique has been used to reduce the dataset up-to optimum important features. We have compared the performance of the developed model using machine learning algorithms and find the best prediction performance using GBC, followed by RF and DT. These algorithmic models capture the trends from various cases of phishing with over R-square, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), in each case.
PubDate: 2023-04-01
- Functional fruit market centres: Their spatial distribution and
hierarchical organization-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Fruit marketing is more challenging than other agricultural commodities due to the product’s high perishability, seasonality, and bulkiness, all of which necessitate special attention and early disposal. Market centres are supposed to be the farmer’s initial point for marketing-related tasks. This work aims to identify the location, spatial arrangement, and hierarchical organization of functional fruit market centres in Kashmir Valley, India. Both primary and secondary data sources were used to achieve the objective’s fundamental premises. By handheld Global Positioning System (GPS), the coordinates of selected fruit market centres were collected, and their distributional pattern was evaluated using Nearest Neighbour Analysis. The result indicates that the selected sites follow a “random distributional pattern” with an Rn value of 1.12. The hierarchical classification was evaluated using functional composite indices calculated by the Davies location quotient method and annual turnover. Among the ten functional fruit market centres, only one is in the highest (1st) order of Hierarchy, two in the 2nd, two in the 3rd, and five in the lowest (4th), depicting the region’s asymmetrical hierarchical organization. The study findings could be influential in formulating strategies to enhance the marketing efficiency of fruit market centres in the region.
PubDate: 2023-04-01
- Extending QGIS processing toolbox for assessing the geometrical properties
of OpenStreetMap data-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract OpenStreetMap (OSM) offers an under-explored crowdsourced geospatial data useful to urban street network researchers for assessing the geometrical properties of spatial data. Urban street network analysis can provide the assessment of spatial dataset through these properties. The lack of a suitable evaluation framework renders problem for its potential users to evaluate the geometrical properties of the data. To overcome this difficulty, the capability of processing toolbox of QGIS has been extended by developing processing scripts using Python. These scripts were further used as components in the graphical modeler. The parameters such as degree centrality, average path length, closeness centrality, betweenness centrality, clustering coefficient, and inter-network indicators are developed to provide insights to the overall nature of the spatial data. For performing the empirical analysis, OSM dataset of five biggest cities of state of Punjab (India) have been analyzed and compared temporally. The results presented the internal geometrical feature’s evaluation of street network in temporal comparison of OSM dataset and its credibility. This study provided the basis for reproducible research by developing components for open source software QGIS. The developed model can be used to asses the geometrical properties assessment by the town planners to identify the prominent nodes, edges and their relationships in the datasets.
PubDate: 2023-04-01
- Assessment of wet season agricultural droughts using monthly MODIS and SAR
data in the red and lateritic zone of West Bengal, India-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Recurrent droughts, particularly in the monsoon seasons, often severely affect the agricultural yield. Such short-lived yet acute droughts lead to crop failure, affecting the livelihood of small and marginal farmers of the sub-humid red and laterite zones (RLZs). The present paper analyses the monthly agricultural droughts during the monsoon seasons in the RLZs using MODIS and SAR data. From the analysis of MODIS-derived indices like NDVI, TCI, TVDI, and ZPET for the years 2005 to 2020, three acute drought years of 2005, 2010, and 2015 were identified as severe to extreme drought conditions which led to crop failures. MODIS-derived drought indices were validated by rainfall (SPI) and evapotranspiration (SPEI) based meteorological indices. Results showed that the satellite-derived evapotranspiration index ZPET and soil moisture index TDVI were better correlated with the SPEI, while the NDVI anomaly was better correlated with the SPI. Ground-level drought conditions prevalent during the monsoon months of June to September in 2015 could be further detected by the SAR backscatter of multi-temporal data from 2015 to 2020. This study provides a theoretical basis for the application of MODIS and SAR data in tandem for identifying short-lived but potentially damaging agricultural droughts during monsoon months.
PubDate: 2023-04-01
- An interactive visualization of location-based reviews using word cloud
and OpenStreetMap for tourism applications-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Massive volumes of online reviews have been generated as cities have grown and information has exploded. The majority of previous research on reviews has focused on debate, bias, and sentiment analysis. There is a lack of visual perception of the spatiotemporal pattern distribution in these reviews. This study attempts to bridge this gap and proposes an interactive visualization system combining Word cloud and OpenStreetMap to allow for visual exploration of spatial features, location mining, and decision making based on reviews. In this visualization, the original context of the reviews can be first explored through the designed interactive word cloud. Then, the geographical distribution information of the reviews will be presented and absorbed rapidly on the geographic map. Our system can guide users not only through the spatial features of online reviews but also efficiently retrieve their original review context by providing an interactive visualization and intuitive results. Thus, it is appropriate as an alternative method by transforming complex and abstract information into a simple and easy-to-understand visual language for online reviews understanding.
PubDate: 2023-04-01
- Exploring spatial pattern of eateries in Calabar City, Cross River State,
Nigeria-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract The study explored the spatial pattern of eateries distribution in Calabar city, Nigeria. Data collection was primarily through primary and secondary sources. The data collected were analysed using descriptive and inferential statistics. The nearest neighbour analysis revealed a clustered pattern of 47 eateries distributed with an Rn value of 0.37, z-score (− 8.23), (P < 0.05). This form of distribution was mainly around the city centre, also known as the central business district. The study further showed a significant inverse relationship between the frequency of patronage and the distance of customers from the eateries (R value of − 0.68 at P < 0.05). This implies that the farther the distance of customers, the decrease the level of patronage of eateries. Furthermore, the study unravelled the major locational factors of eateries, such as security, high population density, and tourism attractions, representing a mean value of 0.8–1.0. The Chi-square (χ2) analysis showed (df = 2) = 125.23, (P < 0.05), implying that patronage of eateries significantly depend on the time of the day. It was recommended that government and business proprietors should consider the fundamental locational factors influencing spatial distribution of eateries, planning and establishment of eatery centres in the city for the sustainability of the business.
PubDate: 2023-04-01
- Deep learning-based framework for vegetation hazard monitoring near
powerlines-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract The increasing popularity of drones has led to their adoption by electric utility companies to monitor intrusive vegetation near powerlines. The study proposes a deep learning-based detection framework compatible with drones for monitoring vegetation encroachment near powerlines which estimates vegetation health and detects powerlines. Aerial image pairs from a drone camera and a commercial-grade multispectral sensor were captured and processed into training and validation datasets which were used to train a Generative Adversarial Network (Pix2Pix model) and a Convolutional Neural Network (YoLov5 model). The Pix2Pix model generated satisfactory synthetic image translations from coloured images to Look-Up Table (LUT) maps whiles the YoLov5 obtained good performance for detecting powerlines in aerial images with precision, recall, mean Average Precision (mAP) @0.5, and mAP0.5:0.95 values are 0.82, 0.76, 0.79 and 0.56 respectively. The proposed vegetation detection framework was able to detect locations of powerlines and generate NDVI estimates represented as LUT maps directly from RGB images captured from aerial images which could serve as a preliminary and affordable alternative to relatively expensive multispectral sensors which are not readily available in developing countries for monitoring and managing the presence and health of trees and dense vegetation within powerline corridors.
PubDate: 2023-03-20
- Examining the relationship between socioeconomic structure and urban
transport network efficiency: a circuity and spatial statistics based
approach-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Access to urban public transportation services is crucial for all city residents. Undoubtedly, more efficient public transportation services should be provided for the needy ones. The study aims to develop a simple yet efficient analytical approach to spatially determine the urban areas that receive inefficient public transportation services. In this study, the spatial distribution of the efficiency of public transportation and its relation to socioeconomic variables (per capita income level and population density) are examined at the neighborhood in Izmir, Turkey, level using circuity. The results from univariate and bivariate Global and Local Moran’s I analyses reveal that the high-efficiency levels are spatially clustered, Higher-income neighborhoods have better public transportation systems compared to lower-income ones. According to Bivariate Local Moran’s I analyses, among the 348 neighborhoods at least 31 and at most 81 neighborhoods are either in a High-High or Low-Low cluster for the four time periods considered. As for the relationship between circuity and density, at least 21 and at most 75 neighborhoods are a part of a cluster. The fact that there is a significant relationship between the efficiency of public transportation and socioeconomic variables calls for alteration in the planning policies regarding urban public transportation supply. Although the variation in public transport efficiency levels across the neighborhoods can partly be attributed to physical conditions, the city should provide equal accessibility and efficiency regardless of the socio-economic status of the neighborhoods. The findings can well be generalized for cities of similar sizes in developing countries.
PubDate: 2023-03-17
- Estimating thickness of Zemu glacier, Sikkim (India) using ice-flow
velocity approach: a geoinformatics based perspective-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract In the present scenario of warming climate, overall health of the glaciers along with sea level rise/fall are directly impacted by glacial dynamics. However due to inaccessible high altitude regions and devastating climate, the in-situ observations are hindered via field excursions. The present study incorporated usability of geographical information system based ice-flow velocity approach using glacier surface velocity and slope for estimating thickness of Zemu glacier in Sikkim. The study revealed thickness of 80 ± 9.6 m to 160 ± 19.2 m near snout followed by 240 ± 28.8 m to 320 ± 38.4 m in upper reaches of accumulation zone of Zemu glacier. However due to gentle slope, thickness ranged between 320 ± 38.4 m and > 400 ± 49.2 m (~ 418 ± 50.16 m) was observed in the central trunk or middle reaches of the glacier. An uncertainty of 12% was observed while calculation the glacier thickness. Relationship between glacier velocity and depth has also been established which has shown inverse characteristics due to variability of bed topography and drag effects. Proper validation of results for each study with existing field observations and literatures depicted the utility and correctness of the present study via satellite based observations.
PubDate: 2023-03-15
- Short-term exposure to air pollution and COVID-19 in India:
spatio-temporal analysis of relative risk from 20 metropolitan cities-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract The present study investigates the possible association between major air pollutants and COVID-19. We hypothesized that the post-lockdown surge in air pollution is the major cause of the increment in COVID-19 cases and deaths. The statistical results showed that pollutant concentrations of PM2.5 (20%), PM10(24%), SO2 (12%), and O3 (19%) were raised. So, we attempted to quantify the relative risk due to all major air pollutants by fitting generalized additive models. The results suggest that the pollution concentration escalated the COVID-19 cases and deaths. The pooled study suggests that for every 10 μg/m3 increment in pollutant concentration, an increment of COVID-19 cases is observed for PM2.5 (3%), PM10 (1%), SO2 (7.7%), and O3 (10%). Similarly, there is an increment in COVID-19 deaths for PM2.5 (2.8%), PM10 (1%), SO2 (4.5%), and O3 (7.2%). The spatial maps of relative risk revealed the most vulnerable regions due to each pollutant, thus steering the policymakers to implement region-specific mitigation strategies.
PubDate: 2023-03-14
- Leveraging GIS to deploy demand-driven public charging infrastructure in
an Indian Metropolitan city-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract The rise of electric vehicle (EV) usage in India is hindered by insufficient public charging infrastructure, which is considered a key hindrance to widespread EV adoption. To overcome this, it is essential to deploy public charging infrastructure in a demand-driven approach to meet the needs of EV users. This study provides a framework for developing a public charging infrastructure plan using geographic information systems, where demand for public charging is calculated based on ten indirect parameters such as population density, petrol stations, public spaces, and others. The proposed methodology was demonstrated through a case study in the city of Surat, where geospatial data was used to determine local charging demand. The analysis results produced a public charging demand map for 2025, which showed high EV charging demand in the core city region and along key transport corridors. The study highlights the need for policy interventions to effectively utilize installed charging infrastructure. The geospatial analysis is a useful tool for determining optimal locations for public charging infrastructure deployment, which can help decision-makers use the city's resources optimally.
PubDate: 2023-03-14
- A sea lion optimized microstrip patch antenna for enhancing the energy
efficiency of the WSN-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract There is a growing trend toward the use of intelligent Wireless Sensor Networks for a variety of applications (WSNs). One of the main difficulties with WSNs is energy efficiency (EE). For energy saving, the directional antennas (Das) utilization is radiated in a specified direction but they reduce beam width by focusing power in that direction. Consequently, for enhancing EE and the longevity of the network, a low-powered double T slotted Micro Strip Patch Antenna (MSPA) was proposed in this paper. In the ‘4’ phases, the antenna is designed. Along with this, a new directional ‘2’ T-shaped MSPA with the aim of enhancing EE is designed. The initial parameters of the designed antenna are fed into the Sea Lion Optimization Algorithm to acquire an optimal parameter for obtaining a better frequency range. The proposed model attains the return loss of −19.2db, a gain of 2.5db, and directivity of 5.79db when contrasted with prevailing models. By achieving this, the paper exhibits the developed system’s superiority and could be a well-energy-efficient antenna in WSN. To encompass building an optimal antenna for WSNs, the work can be enlarged in the future by utilizing meta-material structure gain.
PubDate: 2023-02-27
- Exploring LULC changes in Pakhal Lake area, Telangana, India using QGIS
MOLUSCE plugin-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Dynamic processes such as environmental, economic, and social factors influence land use and land cover (LULC) changes, with temporal and spatial variations. This study aims to identify changes in LULC and predict future trends in the Pakhal Lake area in Peninsular India. Satellite images for the years from 2016 to 2022 were used for LULC classification using deep learning with Sentinel − 2 imagery in Google Earth Engine (GEE). Dynamic World dataset is used to classify the LULC changes of the study area with a 10 m near-real-time dataset. Images were classified based on six different LULC classes, namely water, vegetation, flooded vegetation, agriculture, built-up area, and bare land. The Cellular Automata–Artificial Neural Network (CA − ANN) technique was used to predict LULC changes. QGIS plugin MOLUSCE with Multi-Layer Perception (MLP), was used to predict and determine potential LULC changes for 2025 and 2028. The overall Kappa coefficient value of 0.78, and an accuracy of 82% indicated good results for LULC changes and projected maps for 2025. Prediction of LULC changes using MLP − ANN for the years 2025 and 2028 showed increase in agriculture, built-up areas, and barren land. The results of the study will be useful to develop better management techniques of natural resources.
PubDate: 2023-02-27
- Correction: Spatio-temporal growth of a traditional urban centre in
Nigeria-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
PubDate: 2023-02-27
- Cost–benefit analysis of remote sensing data types for mapping
mosquito breeding sites-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Environmentally friendly biological mosquito control by Bacillus thuringiensis var. israelensis formulations needs appropriate breeding maps. The mapping accuracy depends on the quality of the used remote sensing data. Further, the mapping is expected to be cost-effective. Our aim was to study the effect of the quality of various remote sensing data on the applicability of the maps. We depicted larval habitats by manual interpretation in Quantum GIS 3.16.1 software using remote sensing data of SENTINEL, Google Earth, commercial geoTIFF RGB orthophoto, individual unoccupied aerial systems (UAS) RGB, and multispectral mosaics. Based on our results, after classification of the target area by sorting, mixed-use of remote sensing data is required to achieve a highly cost-efficient mapping: RGB aerial photographs with 0.5 m per pixel resolution can be used efficiently in areas dominated by grassland habitats, while forest areas need customised footage taken by UAS or drones during the foliage-free period (15 cm per pixel resolution, multispectral technique). Our cost–benefit analysis showed that the aim-optimised method could reduce investment to 6–8% and the cost of data collection to 20–50% of the highest budget. This result is significant for all participants of biological mosquito control.
PubDate: 2023-02-23
- Migration in propinquity with development: a spatial analysis of Kashmir
Valley, India-
Free pre-print version: Loading...
Rate this result:
What is this?
Please help us test our new pre-print finding feature by giving the pre-print link a rating.
A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Migration is the primary demographic process that shapes where people live and plays an essential role in how humans have changed over time. People migrate for various reasons, but the predicted economic disparity between industrialized and developing regions is a powerful motivator for people to relocate. As a result, migration has an impact on development, and development also affects migration. Because of the vast differences in development among India's regions, the future of population dynamics will rely more on migration than fertility and mortality processes. The present paper analyzes the spatial patterns of internal migration, variations in the level of development, and the propinquity between the two. Secondary data were used from the census of India, government reports, and research publications. Data were analysed using Karl Pearson's correlation coefficient and a t-test to test the observed correlation's significance. The results revealed a high positive correlation between internal migration and levels of development (r = 0.791). Out of thirty-three development variables, only sixteen have a higher significant relationship with internal migration, which mainly determines population characteristics, education facilities, employment opportunities, and infrastructural facilities.
PubDate: 2023-02-11
|
Hybrid journal (It can contain Open Access articles)
