scholarly journals Spatio-temporal Analysis of Maximum and Minimum Temperature in Bangladesh

2015 ◽  
Vol 7 (2) ◽  
pp. 73-77 ◽  
Author(s):  
MN Uddin ◽  
MSA Mondal ◽  
NMR Nasher
Keyword(s):  
Spatial Distribution ◽  
Minimum Temperature ◽  
Statistical Significance ◽  
Temporal Analysis ◽  
Temperature Data ◽  
Spatially Distributed ◽  
Spatio Temporal ◽  
Gis Environment ◽  
Maximum Temperatures ◽  
Kendall Method

The analysis of annual mean maximum and annual mean minimum temperature data are studied in GIS environment, obtained from 34 meteorological stations scattered throughout the Bangladesh from 1948 to 2013. IDW method was used for the spatial distribution of temperature over the study area, using ArcGIS 10.2 software. Possible trends in the spatially distributed temperature data were examined, using the non-parametric Mann-Kendall method with statistical significance, and the magnitudes of available trends were determined using Sen’s method in ArcMap depiction. The findings of the study show positive trends in annual mean maximum temperatures with 90%, 95%, 99% and 99.9% significance levels.DOI: http://dx.doi.org/10.3329/jesnr.v7i2.22210 J. Environ. Sci. & Natural Resources, 7(2): 73-77 2014

scholarly journals Spatio-Temporal Analysis of Urban Crime Pattern and its Implication for Abuja Municipal Area Council, Nigeria

2017 ◽  
Vol 49 (2) ◽  
pp. 145
Author(s):  
Taiye Oluwafemi Adewuyi ◽  
Patrick Ali Eneji ◽  
Anthonia Silas Baduku ◽  
Emmanuel Ajayi Olofin
Keyword(s):  
Information System ◽  
Spatial Data ◽  
Temporal Analysis ◽  
Urban Crime ◽  
High Rate ◽  
Geographical Information ◽  
Positioning Systems ◽  
Spatio Temporal ◽  
Gis Environment ◽  
Crime Pattern

This study examined the spatio-temporal analysis of urban crime pattern and its implication for Abuja Municipal Area Council of the Federal Capital Territory of Nigeria; it has the aim of using Geographical Information System to improve criminal justice system. The aim was achieved by establishing crime incident spots, types of crime committed, the time it occurred and factors responsible for prevailing crime. The methods for data collection involved Geoinformatics through the use of remote sensing and Global Positioning Systems (GPS) for spatial data. Questionnaires were administered for other attribute information required. The analysis carried out in a Geographic Information System (GIS) environment especially for mapping and the establishment of spatial patterns.  The results indicated that the main types of crime committed were theft and house breaking (42.9%), followed by assault (12.4%), mischief (11.3%), forgery (10.5%), car snatching (9.05%), armed robbery (8.5%), trespass (5.2%) and culpable homicide (0.2%). In terms of hot spots the districts recorded the following: Garki (27.62%), Maitama (25.7%), Utako (24.3%), Wuse (20.9%) and Asokoro district (1.4%) respectively with most of the crime committed during the day time. Many attributed the crimes to mainly high rate of unemployment and poverty (79.1%). Consequently to reduce the crime rate, the socio-economic situation of the city must be improved through properly constructed interventions scheme in areas known to quickly generate employment such as agriculture, small and medium scale enterprises, mining and tourism. 

scholarly journals A spatio-temporal analysis of trends in Northern Hemisphere snow-dominated area and duration, 1971–2014

2017 ◽  
Vol 58 (75pt1) ◽  
pp. 21-35 ◽  
Author(s):  
Michael I. Allchin ◽  
Stephen J. Déry
Keyword(s):  
Northern Hemisphere ◽  
Binary Classification ◽  
Statistical Significance ◽  
Temporal Analysis ◽  
Energy Budgets ◽  
Rutgers University ◽  
Snow Covered Area ◽  
Covered Area ◽  
Spatio Temporal

ABSTRACTSeasonal snow-cover modulates water and energy budgets across large areas of the Northern Hemisphere. Previous research, based on satellite imagery interpreted and curated by the Rutgers University Snow Laboratory, has identified significant negative and positive trends in annual snow-covered duration and area at hemispheric and continental scales between 1971 and 2014. This study uses the same dataset to generate more detailed descriptions of spatial variations in these trends, maps intraannual variations in sign, statistical significance and strength, and quantifies associations with latitude and elevation. It also considers the limitations and uncertainties associated with a binary classification of this type, and the implications for trend magnitudes of adopting alternatives to the conventional assumption of 100% (0%) actual fractional snow-covered area in ‘snow-covered’ (‘snow-free’) spatial units at different stages of the snow-season. This prompts adoption of alternative terminology, referring to ‘snow-dominated’ area and duration. In response to questions about the dataset's veracity raised by some prior studies, it discusses climatological factors of potential relevance in explaining spatio-temporal trend patterns, and considers how biases might possibly have been introduced as a result of extraneous influences.

scholarly journals Comparison of three interpolation methods for the average monthly temperature in the south of Iraqi zone

2019 ◽  
Vol 11 (21) ◽  
pp. 59-66
Author(s):  
Nawal K. Ghazal
Keyword(s):  
Minimum Temperature ◽  
Temperature Data ◽  
Data Sets ◽  
Climate Data ◽  
The South ◽  
Interpolation Methods ◽  
Spatially Distributed ◽  
Model Efficiency ◽  
The Mean ◽  
South Of Iraq

This study focuses on evaluating the suitability of three interpolation methods in terms of their accuracy at climate data for some provinces of south of Iraq. Two data sets of maximum and minimum temperature in February 2008 from nine meteorological stations located in the south of Iraq using three interpolation methods. ArcGIS is used to produce the spatially distributed temperature data by using IDW, ordinary kriging, and spline. Four statistical methods are applied to analyze the results obtained from three interpolation methods. These methods are RMSE, RMSE as a percentage of the mean, Model efficiency (E) and Bias, which showed that the ordinary krigingis the best for this data from other methods by the results that have been obtained .

scholarly journals Spatio-temporal analysis of influenza-like illness and prediction of incidence in high-risk regions, in the United States from 2011 to 2020

2021 ◽  
Author(s):  
Zhijuan Song ◽  
Xiaocan Jia ◽  
Junzhe Bao ◽  
Yongli Yang ◽  
Huili Zhu ◽  
...  
Keyword(s):  
United States ◽  
Statistical Significance ◽  
Temporal Analysis ◽  
The United States ◽  
Incidence Rates ◽  
Influenza Like Illness ◽  
Temporal Cluster ◽  
Control And Prevention ◽  
Spatio Temporal ◽  
And Control

Abstract Introduction: About 8% of Americans get influenza during an average season from the Centers for Disease Control and Prevention in the United States. It is necessary to strengthen the early warning of influenza and the prediction of public health. Methods In this study, we analyzed the characteristics of Influenza-like Illness (ILI) by Geographic Information System and SARIMA model, respectively. Spatio-temporal cluster analysis detected 23 clusters of ILI during the study period. Results The highest incidence of ILI was mainly concentrated in the states of Louisiana, District of Columbia and Virginia. The Local spatial autocorrelation analysis revealed the High-High cluster was mainly located in Louisiana and Mississippi. This means that if the influenza incidence is high in Louisiana and Mississippi, the neighboring states will also have higher influenza incidence rates. The regression model SARIMA(1, 0, 0)(1, 1, 0)52 with statistical significance was obtained to forecast the ILI incidence of Mississippi. Conclusions The study showed, the ILI incidence will begin to increase in the 45th week 2020 and peak in the 6th week 2021. To conclude, notable epidemiological differences were observed across states, indicating that some states should pay more attention to prevent and control respiratory infectious diseases.

scholarly journals Spatio-Temporal Distribution of Flash Floods in Slovenia

Dela ◽  
2016 ◽  
pp. 5-39 ◽  
Author(s):  
Tajan Trobec
Keyword(s):  
Spatial Distribution ◽  
Temporal Distribution ◽  
Temporal Analysis ◽  
Flash Floods ◽  
Seasonal Distribution ◽  
Flash Flooding ◽  
Hilly Area ◽  
Spatio Temporal

This paper examines the spatial distribution of frequency of flash floods along with their seasonal distribution. The spatio-temporal analysis of past flash flooding covered 124 flash floods affecting areas of Slovenia between 1550 and 2015. Flash floods are most common in the mountainous and hilly area of northern Slovenia, which consists of alpine and a large part of subalpine landscapes. Autumnal flash floods occur across most of the country, while summer flash floods are seen mainly in the east. In most parts of the country autumnal flash floods predominate.

scholarly journals Event Detection and Spatio-temporal Analysis of Low-Altitude Unstable Approach

2020 ◽  
Vol 10 (14) ◽  
pp. 4934
Author(s):  
Huabo Sun ◽  
Jiayi Xie ◽  
Yang Jiao ◽  
Rongshun Huang ◽  
Binbin Lu
Keyword(s):  
Spatial Distribution ◽  
Correlation Coefficient ◽  
Spatial Data ◽  
Mainland China ◽  
Temporal Analysis ◽  
Spatial Data Analysis ◽  
Technical Program ◽  
Low Altitude ◽  
Spatio Temporal ◽  
Bad Weather

Low-altitude unstable approach (UA) is one of the crucial risks that threaten flight safety. In this study, we proposed a technical program for detecting low-altitude UA events. The detection logic was to optimize the step-wise regression model with iterative surveys with more than 20 experienced pilots. Accordingly, the frequencies of UA events occurring around each airport in January 2018 were calculated for all the airports within mainland China. Finally, the spatial distribution characteristics of UA events were analyzed via exploratory spatial data analysis. In addition, Pearson’s correlation coefficient and the geographically weighted correlation coefficient were used to explore the correlations between UA frequency and the altitude elevation, wind level, and bad weather. The experimental results revealed that the proposed method can accurately detect the occurrence of low-altitude UA and quantitatively characterize risks. It was found that UA exhibits obvious differences in spatial distribution. Moreover, significantly strong correlations were found between UA and altitude elevation, wind level, and bad weather, and correlation differences were also reflected in different regions in China.

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