Municipal changes in Slovakia. The evidence from spatial data.

2020 ◽  
Vol 11 (1) ◽  
pp. 58-72
Author(s):  
Martin MARIS
Keyword(s):  
Spatial Data ◽  
Time Series Data ◽  
Population Sample ◽  
Series Data ◽  
Annual Growth Rate ◽  
The West ◽  
Compound Annual Growth Rate ◽  
Fast Growing ◽  
The North ◽  
Statistical Criteria

The main objective of the paper is to examine the evolution of spatial patterns of settlement network in Slovakia as a result of population rearrangement among municipalities based on time series data of 1993 - 2017. The objects of the research are municipalities, which during the searched period recorded unusual fast population growth or decline, far exceeding the chosen parameter of the population sample. The primary population sample consists of 2919 municipalities. The experimental samples consist of 563 of fast-growing municipalities and 413 of fast-declining municipalities, based on the chosen statistical criteria, what is the compound annual growth rate. The results have shown that fast-growing municipalities are predominantly situated on the West, surrounding the Bratislava agglomeration, on the North and the East surrounding the Kosice metropolis. Generally, they tend to cluster around the cities on the district and regional levels. Fast-declining municipalities predominantly situated in the Middle, along the Hungarian, Polish, and Ukrainian border on the South and the East of the country, respectively.

scholarly journals TREND, GROWTH RATE AND INSTABILITY ANALYSIS OF PRODUCTION, PRODUCTIVITY, AREA AND SUPPLY OF POTATO IN KAVREPALANCHOK DISTRICT, NEPAL

2021 ◽  
Vol 1 (2) ◽  
pp. 88-87
Author(s):  
Mahesh Rijal ◽  
Rabin Thapa ◽  
Arvind Srivastava ◽  
Gunakeshari Lamsal
Keyword(s):  
Growth Rate ◽  
Time Series Data ◽  
Annual Growth ◽  
Series Data ◽  
Annual Growth Rate ◽  
Wholesale Market ◽  
White Potato ◽  
Instability Analysis ◽  
Compound Annual Growth Rate ◽  
Future Production

A study was carried out to assess the trend of area, production, productivity and supply of potato in Kavre district, Nepal. The time-series data (1999/00 to 2017/18) were collected from the “Statistical Information on Nepalese Agriculture” published yearly by the Ministry of Agriculture and Livestock Development, Nepal and the data of potato (red and white) supply from Kavre to Kalimati wholesale market from 2000/01 to 2019/20 was collected from the official website of Kalimati market. The data were entered and analyzed using Microsoft Excel and XLSTAT. Mann-Kendall test (M-K) and Sen’s slope method were used for trend analysis. The results showed that the potato cultivation area increased by 341.786 ha/year, production increased by 8323.933 Mt/year and productivity increased by 0.231 Mt/ha/year from 1999/00 to 2017/18. Similarly, the red potato supply from Kavre to the Kalimati market increased by 13.412 Mt/year and the white potato supply decreased by 234.174 Mt/year during the period from 2000/01 to 2019/20. The instability analysis showed 34.41%, 41.36% and 11.16%. coefficient of variation for area, production and productivity while red potato and white potato supply showed 11.64% and 107.86% variation. The average annual growth rates for area, production and productivity of potato were 6.02%, 8.83% and 2.43%, respectively. Similarly, growth rate of red potato supply was 3.91% per annum while white potato supply decreased at the compound annual growth rate of 19.61%. Thus, an increasing trend of area, production and productivity and supply of potato along with a positive growth rate for the potato can be seen in the Kavre district. Findings from this study could be used to suggest necessary policy guidelines for future production and marketing strategies of potato in Kavre.

Growth and export status of Indian floriculture: A review

2016 ◽  
Vol 37 (1) ◽  
Author(s):  
Debajit Misra ◽  
Sudip Ghosh
Keyword(s):  
Growth Rate ◽  
Time Series Data ◽  
Series Data ◽  
Annual Growth Rate ◽  
Cut Flowers ◽  
Compound Annual Growth Rate ◽  
Substantial Growth ◽  
The World ◽  
Recent Developments ◽  
Primary Focus

This paper aims at conducting a study on recent developments of floriculture industry in India, particularly in terms of production of cut flowers and the growth of the industry and its trade with the world. Primary focus is on the flowers (loose as well as cut flowers) that are grown commercially. Time series data covering a period of last two decades have been used for the study. The paper also reports on the global trade scenario for floricultural products, indicating the major trading countries and their trade. It is observed that during the period the production of both the loose and cut flowers have been growing at a Compound Annual Growth Rate (CAGR) of 9.92% and 26.66%, respectively. However, the first decade did not see substantial growth in export of floricultural products from India. During the last decade, export increased at a CAGR of 4.33%. India’s share of global floriculture trade at present is only about 0.6%.

scholarly journals An Empirical Mode-Spatial Model for Environmental Data Imputation

Hydrology ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 63 ◽  
Author(s):  
Benjamin Nelsen ◽  
D. Williams ◽  
Gustavious Williams ◽  
Candace Berrett
Keyword(s):  
Time Series ◽  
Spatial Data ◽  
Missing Values ◽  
Time Series Data ◽  
Environmental Data ◽  
Series Data ◽  
Data Imputation ◽  
Accurate Data ◽  
Target Station ◽  
Periodic Components

Complete and accurate data are necessary for analyzing and understanding trends in time-series datasets; however, many of the available time-series datasets have gaps that affect the analysis, especially in the earth sciences. As most available data have missing values, researchers use various interpolation methods or ad hoc approaches to data imputation. Since the analysis based on inaccurate data can lead to inaccurate conclusions, more accurate data imputation methods can provide accurate analysis. We present a spatial-temporal data imputation method using Empirical Mode Decomposition (EMD) based on spatial correlations. We call this method EMD-spatial data imputation or EMD-SDI. Though this method is applicable to other time-series data sets, here we demonstrate the method using temperature data. The EMD algorithm decomposes data into periodic components called intrinsic mode functions (IMF) and exactly reconstructs the original signal by summing these IMFs. EMD-SDI initially decomposes the data from the target station and other stations in the region into IMFs. EMD-SDI evaluates each IMF from the target station in turn and selects the IMF from other stations in the region with periodic behavior most correlated to target IMF. EMD-SDI then replaces a section of missing data in the target station IMF with the section from the most closely correlated IMF from the regional stations. We found that EMD-SDI selects the IMFs used for reconstruction from different stations throughout the region, not necessarily the station closest in the geographic sense. EMD-SDI accurately filled data gaps from 3 months to 5 years in length in our tests and favorably compares to a simple temporal method. EMD-SDI leverages regional correlation and the fact that different stations can be subject to different periodic behaviors. In addition to data imputation, the EMD-SDI method provides IMFs that can be used to better understand regional correlations and processes.

scholarly journals Growth in Vegetable Production and its Decomposition in Karnataka

2021 ◽  
Vol 66 (1) ◽  
Author(s):  
Afrin Zainab Bi
Keyword(s):  
Time Series Data ◽  
Leafy Vegetables ◽  
Series Data ◽  
Vegetable Production ◽  
Annual Growth Rate ◽  
Total Production ◽  
Great Opportunity ◽  
Nutritional Security ◽  
Cole Crops ◽  
Indian Agriculture

Vegetables are important constituents of Indian agriculture and nutritional security. Along with health benefits, vegetables help in flourishing countries economy, as it provides a great opportunity for income and employment generation for the farming sector. The study has an objective to understand the extent of growth each vegetable experiencing and to derive the major factor for the growth in Karnataka, utilizing time-series data. The total area showed an increasing trend over the period with about 40 % increase in a span of two decades. However, figures for increased production were more appealing than its area, as it has shown 60 % increase. Total production of vegetables in Karnataka has increased from 42 lakh tonnes in 1998-99 to 68 lakh tonnes in 2018-19, with an annual growth rate of 3.9 %. The highest growth in production was observed in onion (7.5% annually) followed by tomato and cole crops. The area effect was the most responsible factor for increasing production of tomato, onion, guards, cole crops and other vegetables group. Thus, in effect for overall vegetables, it is 66 % of the total increased production effect. However, for potato and leafy vegetables, only yield effect was found to be positively contributing to the production.

scholarly journals Parameter uncertainty of a dynamic multispecies size spectrum model

2016 ◽  
Vol 73 (4) ◽  
pp. 589-597 ◽  
Author(s):  
Michael A. Spence ◽  
Paul G. Blackwell ◽  
Julia L. Blanchard
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Size Structure ◽  
Management Strategies ◽  
Optimization Methods ◽  
Series Data ◽  
Size Spectrum ◽  
Aquatic Communities ◽  
The North ◽  
Spectrum Model

Dynamic size spectrum models have been recognized as an effective way of describing how size-based interactions can give rise to the size structure of aquatic communities. They are intermediate-complexity ecological models that are solutions to partial differential equations driven by the size-dependent processes of predation, growth, mortality, and reproduction in a community of interacting species and sizes. To be useful for quantitative fisheries management these models need to be developed further in a formal statistical framework. Previous work has used time-averaged data to “calibrate” the model using optimization methods with the disadvantage of losing detailed time-series information. Using a published multispecies size spectrum model parameterized for the North Sea comprising 12 interacting fish species and a background resource, we fit the model to time-series data using a Bayesian framework for the first time. We capture the 1967–2010 period using annual estimates of fishing mortality rates as input to the model and time series of fisheries landings data to fit the model to output. We estimate 38 key parameters representing the carrying capacity of each species and background resource, as well as initial inputs of the dynamical system and errors on the model output. We then forecast the model forward to evaluate how uncertainty propagates through to population- and community-level indicators under alternative management strategies.

Precise identification of maize in the North China Plain based on Sentinel-1A SAR time series data

2018 ◽  
Vol 40 (5-6) ◽  
pp. 1996-2013 ◽  
Author(s):  
Li Li ◽  
Qingling Kong ◽  
Pengxin Wang ◽  
Lan Xun ◽  
Lei Wang ◽  
...  
Keyword(s):  
Time Series ◽  
North China Plain ◽  
North China ◽  
Time Series Data ◽  
Series Data ◽  
The North ◽  
The North China Plain ◽  
Precise Identification

scholarly journals INFASI, PENGANGGURAN DAN KEMISKINAN DI PROVINSI MALUKU UTARA

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Dewi Permatasari
Keyword(s):  
Time Series ◽  
Path Analysis ◽  
Time Series Data ◽  
Positive Impact ◽  
Poverty Rate ◽  
Series Data ◽  
High Unemployment ◽  
The North ◽  
Poverty Levels ◽  
Positive Effect

Tinjauan kemiskinan dari dimensi ekonomi ini diartikan sebagai ketidakmampuan seseorang untuk mendapatkan mata pencaharian yang mapan dan memberikan penghasilan yang layak untuk menunjang hidupnya secara berkesinambungan. Kemiskinan merupakan salah satu masalah yang menjadi pusat perhatian di negara manapun. Kemiskinan disebabkan oleh berbagai faktor, seperti tingkat investasi yang masih dibawah standar, tingkat pengangguran yang tinggi, dan pertumbuhan ekonomi yang lambat. Tujuan penelitian ini adalah menganalisis pengaruh inflasi terhadap pengangguran dan kemiskinan, dan pengaruh pengangguran terhadap kemiskinan di Provinsi Maluku Utara. Penelitian ini menggunakan data runtun waktu (time series) tahun 2013-2018 dan menggunakan analisis jalur (path analysis). Hasil penelitian menunjukkan bahwa inflasi berpengaruh positif terhadap tingkat pengangguran dan kemiskinan di Provinsi Maluku Utara. Kemudian, pengangguran juga berpengaruh positif terhadap tingkat kemiskinan. Semakin tinggi tingkat inflasi dan pengangguran semakin besar tingkat kemiskinan.Kata kunci: Inflasi, Pengangguran, dan Kemiskinan, Maluku Utara Poverty review from the economic dimensions is interpretedas the inability of a person to obtain an established livelihood and provided a decent income to sustainably support life. Poverty is problems that attention any country. Poverty is caused by various factors, such as low investment, high unemployment, and slow economic growth. The purpose of this study was to analyze the effect of inflation on unemployment and poverty, and the effect of unemployment on poverty in North Maluku Provience. This study uses time series data from 2013 to 2018, and path analysis. The results showed that inflation has a positive effect on increasing unemployment and poverty. High unemployment has a positive impact on poverty levels. The higher level of inflation and unemployment, the higher poverty rate in the North Maluku Provience. Keywords: Inflation, Unemployment, Poverty, North Maluku

scholarly journals Temporal analysis of reported cases of tuberculosis and of tuberculosis-HIV co-infection in Brazil between 2002 and 2012

2016 ◽  
Vol 42 (6) ◽  
pp. 416-422 ◽  
Author(s):  
Renato Simões Gaspar ◽  
◽  
Natália Nunes ◽  
Marina Nunes ◽  
Vandilson Pinheiro Rodrigues ◽  
...  
Keyword(s):  
Time Series Data ◽  
Age Groups ◽  
The Elderly ◽  
Temporal Analysis ◽  
Series Data ◽  
Public Health Issue ◽  
Risk Of Death ◽  
The North ◽  
Geographical Regions ◽  
Risk Of Treatment

ABSTRACT Objective: To investigate the reported cases of tuberculosis and of tuberculosis-HIV co-infection in Brazil between 2002 and 2012. Methods: This was an observational study based on secondary time series data collected from the Brazilian Case Registry Database for the 2002-2012 period. The incidence of tuberculosis was stratified by gender, age group, geographical region, and outcome, as was that of tuberculosis-HIV co-infection. Results: Nationally, the incidence of tuberculosis declined by 18%, whereas that of tuberculosis-HIV co-infection increased by 3.8%. There was an overall decrease in the incidence of tuberculosis, despite a significant increase in that of tuberculosis-HIV co-infection in women. The incidence of tuberculosis decreased only in the 0- to 9-year age bracket, remaining stable or increasing in the other age groups. The incidence of tuberculosis-HIV co-infection increased by 209% in the ≥ 60-year age bracket. The incidence of tuberculosis decreased in all geographical regions except the south, whereas that of tuberculosis-HIV co-infection increased by over 150% in the north and northeast. Regarding the outcomes, patients with tuberculosis-HIV co-infection, in comparison with patients infected with tuberculosis only, had a 48% lower chance of cure, a 50% greater risk of treatment nonadherence, and a 94% greater risk of death from tuberculosis. Conclusions: Our study shows that tuberculosis continues to be a relevant public health issue in Brazil, because the goals for the control and cure of the disease have yet to be achieved. In addition, the sharp increase in the incidence of tuberculosis-HIV co-infection in women, in the elderly, and in the northern/northeastern region reveals that the population of HIV-infected individuals is rapidly becoming more female, older, and more impoverished.

scholarly journals A Double-Channel Hybrid Deep Neural Network Based on CNN and BiLSTM for Remaining Useful Life Prediction

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7109
Author(s):  
Chengying Zhao ◽  
Xianzhen Huang ◽  
Yuxiong Li ◽  
Muhammad Yousaf Iqbal
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Spatial Data ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Series Data ◽  
Useful Life ◽  
Lstm Network ◽  
Double Channel

In recent years, prognostic and health management (PHM) has played an important role in industrial engineering. Efficient remaining useful life (RUL) prediction can ensure the development of maintenance strategies and reduce industrial losses. Recently, data-driven based deep learning RUL prediction methods have attracted more attention. The convolution neural network (CNN) is a kind of deep neural network widely used in RUL prediction. It shows great potential for application in RUL prediction. A CNN is used to extract the features of time-series data according to the spatial feature method. This way of processing features without considering the time dimension will affect the prediction accuracy of the model. On the contrary, the commonly used long short-term memory (LSTM) network considers the timing of the data. However, compared with CNN, it lacks spatial data extraction capabilities. This paper proposes a double-channel hybrid prediction model based on the CNN and a bidirectional LSTM network to avoid those drawbacks. The sliding time window is used for data preprocessing, and an improved piece-wise linear function is used for model validating. The prediction model is evaluated using the C-MAPSS dataset provided by NASA. The predicted results show the proposed prediction model to have a better prediction performance compared with other state-of-the-art models.

scholarly journals Tracking Atlantic bluefin tuna from foraging grounds off the west coast of Ireland

2020 ◽  
Vol 77 (6) ◽  
pp. 2066-2077
Author(s):  
Thomas W Horton ◽  
Barbara A Block ◽  
Alan Drumm ◽  
Lucy A Hawkes ◽  
Macdara O’Cuaig ◽  
...  
Keyword(s):  
North Atlantic ◽  
West Coast ◽  
Time Series Data ◽  
Fork Length ◽  
Bluefin Tuna ◽  
Series Data ◽  
Future Research ◽  
The West ◽  
Northeast Atlantic ◽  
Atlantic Bluefin Tuna

Abstract Pop-up archival tags (n = 16) were deployed on Atlantic bluefin tuna (ABT) off the west coast of Ireland in October and November 2016 (199–246 cm curved fork length), yielding 2799 d of location data and 990 and 989 d of depth and temperature time-series data, respectively. Most daily locations (96%, n = 2651) occurred east of 45°W, the current stock management boundary for ABT. Key habitats occupied were the Bay of Biscay and the Central North Atlantic, with two migratory patterns evident: an east-west group and an eastern resident group. Five out of six tags that remained attached until July 2017 returned to the northeast Atlantic after having migrated as far as the Canary Islands, the Mediterranean Sea (MEDI) and the Central North Atlantic. Tracked bluefin tuna exhibited a diel depth-use pattern occupying shallower depths at night and deeper depths during the day. Four bluefin tuna visited known spawning grounds in the central and western MEDI, and one may have spawned, based on the recovered data showing oscillatory dives transecting the thermocline on 15 nights. These findings demonstrate the complexity of the aggregation of ABT off Ireland and, more broadly in the northeast Atlantic, highlighting the need for dedicated future research to conserve this important aggregation.

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