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.

Spatial Econometric Analysis of Cultivated Land Change and its Influencing Factors in the Poyang Lake Eco-Economics Zone

2013 ◽  
Vol 864-867 ◽  
pp. 2659-2664
Author(s):  
Peng Wang ◽  
Qu Liu ◽  
Hua Lin Xie
Keyword(s):  
Spatial Distribution ◽  
Influencing Factors ◽  
Spatial Data ◽  
Poyang Lake ◽  
Spatial Data Analysis ◽  
Cultivated Land ◽  
Land Change ◽  
Spatial Econometric ◽  
Agricultural Growth ◽  
Spatio Temporal

Spatio-temporal pattern of cultivated land change and its influencing factors in the Poyang Lake Ecological Economic Zone were conducted by exploratory spatial data analysis and spatial autocorrelation analysis. Results show that there is an obvious correlation for the spatial distribution of cultivated land in the Poyang Lake Eco-economics Zone. Its value of Morans I reduced from 0.4574 in 2002 to 0.4092 in 2008, and then increased to 0.4352 in 2009, which roughly presented a "U" type distribution. Total population is the most important factor that affecting the change of cultivated areas in the Poyang Lake Eco-economics Zone. Agricultural growth, average wage of urban residents and the fixed assets investment are also the main driving factors. Spatial auto-regression model is an effective tool for revaluating the spatial distribution of regional cultivated land, and revealing the evolution mechanisms of cultivated land.

scholarly journals Spatial Agglomeration of China’s Forest Products Manufacturing Industry: Measurement, Characteristics and Determinants

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1006
Author(s):  
Zhenhuan Chen ◽  
Hongge Zhu ◽  
Wencheng Zhao ◽  
Menghan Zhao ◽  
Yutong Zhang
Keyword(s):  
Spatial Distribution ◽  
Spatial Data ◽  
Large Scale ◽  
Manufacturing Industry ◽  
Economies Of Scale ◽  
Forest Products ◽  
Spatial Data Analysis ◽  
Forest Protection ◽  
Spatial Agglomeration ◽  
Continuous Space

China’s forest products manufacturing industry is experiencing the dual pressure of forest protection policies and wood scarcity and, therefore, it is of great significance to reveal the spatial agglomeration characteristics and evolution drivers of this industry to enhance its sustainable development. Based on the perspective of large-scale agglomeration in a continuous space, in this study, we used the spatial Gini coefficient and standard deviation ellipse method to investigate the spatial agglomeration degree and location distribution characteristics of China’s forest products manufacturing industry, and we used exploratory spatial data analysis to investigate its spatial agglomeration pattern. The results show that: (1) From 1988 to 2018, the degree of spatial agglomeration of China’s forest products manufacturing industry was relatively low, and the industry was characterized by a very pronounced imbalance in its spatial distribution. (2) The industry has a very clear core–periphery structure, the spatial distribution exhibits a “northeast-southwest” pattern, and the barycenter of the industrial distribution has tended to move south. (3) The industry mainly has a high–high and low–low spatial agglomeration pattern. The provinces with high–high agglomeration are few and concentrated in the southeast coastal area. (4) The spatial agglomeration and evolution characteristics of China’s forest products manufacturing industry may be simultaneously affected by forest protection policies, sources of raw materials, international trade and the degree of marketization. In the future, China’s forest products manufacturing industry should further increase the level of spatial agglomeration to fully realize the economies of scale.

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 Approach to Urban Environmental Justice Using Exploratory Spatial Data Analysis. The Case of Valencia’s Monumental Trees

2020 ◽  
Vol 12 (18) ◽  
pp. 7760
Author(s):  
Alfonso Gallego-Valadés ◽  
Francisco Ródenas-Rigla ◽  
Jorge Garcés-Ferrer
Keyword(s):  
Spatial Distribution ◽  
Data Analysis ◽  
Environmental Justice ◽  
Spatial Data ◽  
Urban Trees ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
The Public ◽  
Object Of Study ◽  
The City

Environmental justice has been a relevant object of analysis in recent decades. The generation of patterns in the spatial distribution of urban trees has been a widely addressed issue in the literature. However, the spatial distribution of monumental trees still constitutes an unknown object of study. The aim of this paper was to analyse the spatial distribution of the monumental-tree heritage in the city of Valencia, using Exploratory Spatial Data Analysis (ESDA) methods, in relation to different population groups and to discuss some implications in terms of environmental justice, from the public-policy perspective. The results show that monumental trees are spatially concentrated in high-income neighbourhoods, and this fact represents an indicator of environmental inequality. This diagnosis can provide support for decision-making on this matter.

scholarly journals Spatial and spatio-temporal statistical analyses of retinal images: a review of methods and applications

2020 ◽  
Vol 5 (1) ◽  
pp. e000479
Author(s):  
Wenyue Zhu ◽  
Ruwanthi Kolamunnage-Dona ◽  
Yalin Zheng ◽  
Simon Harding ◽  
Gabriela Czanner
Keyword(s):  
Statistical Methods ◽  
Spatial Data ◽  
Temporal Analysis ◽  
Statistical Analyses ◽  
Retinal Images ◽  
Imaging Data ◽  
Spatial Correlations ◽  
Clinical Variables ◽  
Text Information ◽  
Spatio Temporal

BackgroundClinical research and management of retinal diseases greatly depend on the interpretation of retinal images and often longitudinally collected images. Retinal images provide context for spatial data, namely the location of specific pathologies within the retina. Longitudinally collected images can show how clinical events at one point can affect the retina over time. In this review, we aimed to assess statistical approaches to spatial and spatio-temporal data in retinal images. We also review the spatio-temporal modelling approaches used in other medical image types.MethodsWe conducted a comprehensive literature review of both spatial or spatio-temporal approaches and non-spatial approaches to the statistical analysis of retinal images. The key methodological and clinical characteristics of published papers were extracted. We also investigated whether clinical variables and spatial correlation were accounted for in the analysis.ResultsThirty-four papers that included retinal imaging data were identified for full-text information extraction. Only 11 (32.4%) papers used spatial or spatio-temporal statistical methods to analyse images, others (23 papers, 67.6%) used non-spatial methods. Twenty-eight (82.4%) papers reported images collected cross-sectionally, while 6 (17.6%) papers reported analyses on images collected longitudinally. In imaging areas outside of ophthalmology, 19 papers were identified with spatio-temporal analysis, and multiple statistical methods were recorded.ConclusionsIn future statistical analyses of retinal images, it will be beneficial to clearly define and report the spatial distributions studied, report the spatial correlations, combine imaging data with clinical variables into analysis if available, and clearly state the software or packages used.

scholarly journals Spatial distribution of pedestrian-motor vehicle collisions before and after pedestrian countdown signal installation in Toronto, Canada

2017 ◽  
Vol 25 (2) ◽  
pp. 110-115 ◽  
Author(s):  
Linda Rothman ◽  
Marie-Soleil Cloutier ◽  
Alison K Macpherson ◽  
Sarah A Richmond ◽  
Andrew William Howard
Keyword(s):  
Spatial Distribution ◽  
Spatial Patterns ◽  
Spatial Data ◽  
Motor Vehicle ◽  
Spatial Data Analysis ◽  
Motor Vehicle Collisions ◽  
Vehicle Collisions ◽  
Point Patterns ◽  
Kernel Density Estimates ◽  
Before And After

BackgroundPedestrian countdown signals (PCS) have been installed in many cities over the last 15 years. Few studies have evaluated the effectiveness of PCS on pedestrian motor vehicle collisions (PMVC). This exploratory study compared the spatial patterns of collisions pre and post PCS installation at PCS intersections and intersections or roadways without PCS in Toronto, and examined differences by age.MethodsPCS were installed at the majority of Toronto intersections from 2007 to 2009. Spatial patterns were compared between 4 years of police-reported PMVC prior to PCS installation to 4 years post installation at 1864 intersections. The spatial distribution of PMVC was estimated using kernel density estimates and simple point patterns examined changes in spatial patterns overall and stratified by age. Areas of higher or lower point density pre to post installation were identified.ResultsThere were 14 911 PMVC included in the analysis. There was an overall reduction in PMVC post PCS installation at both PCS locations and non-PCS locations, with a greater reduction at non-PCS locations (22% vs 1%). There was an increase in PMVC involving adults (5%) and older adults (9%) at PCS locations after installation, with increased adult PMVC concentrated downtown, and older adult increases occurring throughout the city following no spatial pattern. There was a reduction in children’s PMVC at both PCS and non-PCS locations, with greater reductions at non-PCS locations (35% vs 48%).ConclusionsResults suggest that the effects of PCS on PMVC may vary by age and location, illustrating the usefulness of exploratory spatial data analysis approaches in road safety. The age and location effects need to be understood in order to consistently improve pedestrian mobility and safety using PCS.

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 Spatial-temporal analysis and visualization of scarlet fever in mainland China from 2004 to 2017

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Wei-tong Li ◽  
Rui-hua Feng ◽  
Tong Li ◽  
Yan-bing Du ◽  
Nan Zhou ◽  
...  
Keyword(s):  
Surface Analysis ◽  
Scarlet Fever ◽  
Spatial Clustering ◽  
National Level ◽  
Mainland China ◽  
Temporal Distribution ◽  
Temporal Analysis ◽  
Trend Surface Analysis ◽  
Trend Surface ◽  
Spatio Temporal

This study retrospectively analyzed the spatio-temporal distribution and spatial clustering of scarlet fever in mainland China from 2004 to 2017. In recent years, the incidence of scarlet fever is increasing. Previous studies on the spatial distribution of scarlet fever in China are mainly focused at the provincial and municipal levels, and there is few systematic report on the spatial and temporal distribution characteristics of scarlet fever on the national level. Based on the incidence information of scarlet fever in mainland China between 2004 and 2017 collected from the China Center for Disease Control, this paper systematically explored the Spatio-temporal distribution of scarlet fever by three methods, contains spatial autocorrelation analysis, Spatio-temporal scanning analysis, and trend surface analysis. The results demonstrate that the incidence of scarlet fever varies by seasons, which is in line with double-peak distribution.The first peak generally occurs from May to June and the second one from November to December, while February and August is the lowest period of incidence. Trend surface analysis indicates that the incidence of scarlet fever in northern China is higher than the south, slightly higher in western compared to the east, and lower in the central part. Additionally, the results show that the clustering regions of scarlet fever centrally distributed in the northeast, northwest, north china and some provinces in the east, such as Zhejiang, Shanghai, Shandong, and Jiangsu.       

scholarly journals Covid-19 Dynamic Monitoring and Real-Time Spatio-Temporal Forecasting

2021 ◽  
Vol 9 ◽  
Author(s):  
Cecilia Cordeiro da Silva ◽  
Clarisse Lins de Lima ◽  
Ana Clara Gomes da Silva ◽  
Eduardo Luiz Silva ◽  
Gabriel Souza Marques ◽  
...  
Keyword(s):  
Linear Regression ◽  
Real Time ◽  
Correlation Coefficient ◽  
Temporal Analysis ◽  
Support Vector ◽  
Multilayer Perceptrons ◽  
Thematic Maps ◽  
Transition Range ◽  
Spatio Temporal ◽  
Health Managers

Background: Periodically, humanity is often faced with new and emerging viruses that can be a significant global threat. It has already been over a century post—the Spanish Flu pandemic, and we are witnessing a new type of coronavirus, the SARS-CoV-2, which is responsible for Covid-19. It emerged from the city of Wuhan (China) in December 2019, and within a few months, the virus propagated itself globally now resulting more than 50 million cases with over 1 million deaths. The high infection rates coupled with dynamic population movement demands for tools, especially within a Brazilian context, that will support health managers to develop policies for controlling and combating the new virus.Methods: In this work, we propose a tool for real-time spatio-temporal analysis using a machine learning approach. The COVID-SGIS system brings together routinely collected health data on Covid-19 distributed across public health systems in Brazil, as well as taking to under consideration the geographic and time-dependent features of Covid-19 so as to make spatio-temporal predictions. The data are sub-divided by federative unit and municipality. In our case study, we made spatio-temporal predictions of the distribution of cases and deaths in Brazil and in each federative unit. Four regression methods were investigated: linear regression, support vector machines (polynomial kernels and RBF), multilayer perceptrons, and random forests. We use the percentage RMSE and the correlation coefficient as quality metrics.Results: For qualitative evaluation, we made spatio-temporal predictions for the period from 25 to 27 May 2020. Considering qualitatively and quantitatively the case of the State of Pernambuco and Brazil as a whole, linear regression presented the best prediction results (thematic maps with good data distribution, correlation coefficient >0.99 and RMSE (%) <4% for Pernambuco and around 5% for Brazil) with low training time: [0.00; 0.04 ms], CI 95%.Conclusion: Spatio-temporal analysis provided a broader assessment of those in the regions where the accumulated confirmed cases of Covid-19 were concentrated. It was possible to differentiate in the thematic maps the regions with the highest concentration of cases from the regions with low concentration and regions in the transition range. This approach is fundamental to support health managers and epidemiologists to elaborate policies and plans to control the Covid-19 pandemics.

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