From Sticks to Carrots: Electoral Manipulation in Africa, 1986–2012

2015 ◽  
Vol 50 (3) ◽  
pp. 521-548 ◽  
Author(s):  
Carolien van Ham ◽  
Staffan I. Lindberg
Keyword(s):  
Time Series ◽  
Vote Buying ◽  
Medium Term ◽  
Cross Sectional ◽  
Data Set ◽  
Trade Offs ◽  
The Future ◽  
National Leaders ◽  
Money In Politics

Over 90 per cent of the world’s states currently select their national leaders through multiparty elections. However, in Africa the quality of elections still varies widely, ranging from elections plagued by violence and fraud to elections that are relatively ‘free and fair’. Yet, little is known about trade-offs between different strategies of electoral manipulation and the differences between incumbent and opposition actors’ strategies. We theorize that choices for specific types of manipulation are driven by available resources and cost considerations for both incumbents and opposition actors, and are mutually responsive. We also suggest that costs of manipulative strategies are shaped by the level of democratization. We test our hypotheses on a time series, cross-sectional data set with observations for 286 African elections from 1986 to 2012. We find that democratization makes ‘cheap’ forms of electoral manipulation available to incumbents such as intimidation and manipulating electoral administration less viable, thus leading to increases in vote buying. The future of democracy in Africa thus promises elections where the administration of elections becomes better and better but at the same time vote buying will increase. Not all things go together, at least not all the time. The future of democracy in Africa will mean more money in politics, more patronage and more clientelistic offers thrown around, at least in the short to medium term.

scholarly journals STUDY OF THE INFLUENCE OF CHANGING THE PARAMETERS OF THE ARIMA MODEL ON THE QUALITY OF THE FORECAST FOR SHORT DATA SETS

2021 ◽  
Vol 21 (3) ◽  
pp. 36-46
Author(s):  
M.N. Fel’ker ◽  
◽  
V.V. Chesnov
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Arima Model ◽  
Annual Data ◽  
Data Sets ◽  
Arima Models ◽  
Data Set ◽  
Series Analysis ◽  
The Future

Time series, i.e. data collected at various times. The data collection segments may differ de-pending on the task. Time series are used for decision making. Time series analysis allows you to get some result that will determine the format of the decision. Time series analysis was carried out in very ancient times, for example, various calendars became a consequence of the analysis. Later, time series analysis was applied to study and forecast economic, social and other systems. Time se-ries appeared a long time ago. Once upon a time, ancient Babylonian astronomers, studying the po-sition of the stars, discovered the frequency of eclipses, which allowed them to predict their appearance in the future. Later, the analysis of time series, in a similar way, led to the creation of various calen-dars, for example, harvest calendars. In the future, in addition to natural areas, social and economic ones were added. Aim. Search for classification patterns of time series, allowing to understand whether it is possible to apply the ARIMA model for their short-term (3 counts) forecast. Materials and methods. Special software with ARIMA implementation and all need services is made. We examined 59 data sets with a short length and step equal a year, less than 20 values in the paper. The data was processed using Python libraries: Statsmodels and Pandas. The Dickey – Fuller test was used to de-termine the stationarity of the series. The stationarity of the time series allows for better forecasting. The Akaike information criterion was used to select the best model. Recommendations for a rea-sonable selection of parameters for adjusting ARIMA models are obtained. The dependence of the settings on the category of annual data set is shown. Conclusion. After processing the data, four categories (patterns) of year data sets were identified. Depending on the category ranges of parame-ters were selected for tuning ARIMA models. The suggested ranges will allow to determine the starting parameters for exploring similar datasets. Recommendations for improving the quality of post-forecast and forecast using the ARIMA model by adjusting the settings are given.

scholarly journals Health-related quality of life and mental health in the medium-term aftermath of the Prestige oil spill in Galiza (Spain): a cross-sectional study

2007 ◽  
Vol 7 (1) ◽  
Author(s):  
José Miguel Carrasco ◽  
Beatriz Pérez-Gómez ◽  
Maria José García-Mendizábal ◽  
Virginia Lope ◽  
Nuria Aragonés ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Cross Sectional Study ◽  
Health Related Quality ◽  
Sectional Study ◽  
Medium Term ◽  
Cross Sectional ◽  
Prestige Oil Spill ◽  
Related Quality ◽  
Health Related

scholarly journals The Irminger Sea and the Iceland Sea time series measurements of sea water carbon and nutrient chemistry 1983–2008

2010 ◽  
Vol 2 (1) ◽  
pp. 99-104 ◽  
Author(s):  
J. Olafsson ◽  
S. R. Olafsdottir ◽  
A. Benoit-Cattin ◽  
T. Takahashi
Keyword(s):  
Time Series ◽  
Surface Waters ◽  
Sea Water ◽  
Dissolved Inorganic Carbon ◽  
Data Set ◽  
Carbon Chemistry ◽  
Irminger Sea ◽  
Nutrient Chemistry ◽  
Seawater Pco2

Abstract. This paper describes the ways and means of assembling and quality controling the Irminger Sea and Iceland Sea time-series biogeochemical data which are included in the CARINA data set. The Irminger Sea and the Iceland Sea are hydrographically different regions where measurements of sea water carbon and nutrient chemistry were started in 1983. The sampling is seasonal, four times a year. The carbon chemistry is studied with measurements of the partial pressure of carbon dioxide in seawater, pCO2, and total dissolved inorganic carbon, TCO2. The carbon chemistry data are for surface waters only until 1991 when water column sampling was initiated. Other measured parameters are salinity, dissolved oxygen and the inorganic nutrients nitrate, phosphate and silicate. Because of the CARINA criteria for secondary quality control, depth >1500 m, the IRM-TS could not be included in the routine QC and the IS-TS only in a limited way. However, with the information provided here, the quality of the data can be assessed, e.g. on the basis of the results obtained with the use of reference materials.

scholarly journals The Irminger Sea and the Iceland Sea time series measurements of sea water carbon and nutrient chemistry 1983–2006

2009 ◽  
Vol 2 (1) ◽  
pp. 477-492 ◽  
Author(s):  
J. Olafsson ◽  
S. R. Olafsdottir ◽  
A. Benoit-Cattin ◽  
T. Takahashi
Keyword(s):  
Time Series ◽  
Surface Waters ◽  
Sea Water ◽  
Dissolved Inorganic Carbon ◽  
Data Set ◽  
Carbon Chemistry ◽  
Irminger Sea ◽  
Nutrient Chemistry ◽  
Seawater Pco2

Abstract. This paper describes the ways and means of assembling and quality controling the Irminger Sea and Iceland Sea time-series biogeochemical data which are included in the CARINA data set. The Irminger Sea and the Iceland Sea are hydrographically different regions where measurements of sea water carbon and nutrient chemistry were started in 1983. The sampling is seasonal, four times a year. The carbon chemistry is studied with measurements of the partial pressure of carbon dioxide in seawater, pCO2, and total dissolved inorganic carbon, TCO2. The carbon chemistry data are for surface waters only until 1994 when water column sampling was initiated. Other measured parameters are salinity, dissolved oxygen and the inorganic nutrients nitrate, phosphate and silicate. Because of the CARINA criteria for secondary quality control, depth >1500 m, the IRM-TS could not be included in the routine QC and the IS-TS only in a limited way. However, with the information provided here, the quality of the data can be assessed e.g. on the basis of the results obtained with the use of reference materials.

scholarly journals The SPARC water vapour assessment II: Comparison of stratospheric and lower mesospheric water vapour time series observed from satellites

2018 ◽  
Author(s):  
Farahnaz Khosrawi ◽  
Stefan Lossow ◽  
Gabriele P. Stiller ◽  
Karen H. Rosenlof ◽  
Joachim Urban ◽  
...  
Keyword(s):  
Time Series ◽  
Water Vapour ◽  
Data Sets ◽  
Data Set ◽  
The Future ◽  
Modelling Studies ◽  
The Difference ◽  
The Tropics ◽  
The Antarctic ◽  
Satellite Instruments

Abstract. Time series of stratospheric and lower mesospheric water vapour using 33 data sets from 15 different satellite instruments were compared in the framework of the second SPARC (Stratosphere-troposphere Processes And their Role in Climate) water vapour assessment (WAVAS-II). This comparison aimed to provide a comprehensive overview of the typical uncertainties in the observational database that can be considered in the future in observational and modelling studies addressing e.g stratospheric water vapour trends. The time series comparisons are presented for the three latitude bands, the Antarctic (80°–70° S), the tropics (15° S–15° N) and the northern hemisphere mid-latitudes (50° N–60° N) at four different altitudes (0.1, 3, 10 and 80 hPa) covering the stratosphere and lower mesosphere. The combined temporal coverage of observations from the 15 satellite instruments allowed considering the time period 1986–2014. In addition to the qualitative comparison of the time series, the agreement of the data sets is assessed quantitatively in the form of the spread (i.e. the difference between the maximum and minimum volume mixing ratio among the data sets), the (Pearson) correlation coefficient and the drift (i.e. linear changes of the difference between time series over time). Generally, good agreement between the time series was found in the middle stratosphere while larger differences were found in the lower mesosphere and near the tropopause. Concerning the latitude bands, the largest differences were found in the Antarctic while the best agreement was found for the tropics. From our assessment we find that all data sets can be considered in the future in observational and modelling studies addressing e.g. stratospheric and lower mesospheric water vapour variability and trends when data set specific characteristics (e.g. a drift) and restrictions (e.g. temporal and spatial coverage) are taken into account.

scholarly journals Machine learning forecasting of CR and PRC balance of trade

2020 ◽  
Vol 73 ◽  
pp. 01004
Author(s):  
Tomàš Brabenec ◽  
Petr Šuleř
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Time Series ◽  
Data Set ◽  
Republic Of China ◽  
The People ◽  
The Future ◽  
Balance Of Trade ◽  
Political Economic ◽  
Social Importance

International trade is an important factor of economic growth. While foreign trade has existed throughout the history, its political, economic and social importance has grown significantly in the last centuries. The objective of the contribution is to use machine learning forecasting for predicting the balance of trade of the Czech Republic (CR) and the People´s Republic of China (PRC) through analysing and machine learning forecasting of the CR import from the PRC and the CR export to the PRC. The data set includes monthly trade balance intervals from January 2000 to June 2019. The contribution investigates and subsequently smooths two time series: the CR import from the PRC; the CR export to the PRC. The balance of trade of both countries in the entire monitored period is negative from the perspective of the CR. A total of 10,000 neural networks are generated. 5 neural structures with the best characteristics are retained. Neural networks are able to capture both the trend of the entire time series and its seasonal fluctuations, but it is necessary to work with time series lag. The CR import from the PRC is growing and it is expected to grow in the future. The CR export to the PRC is growing and it is expected to grow in the future, but its increase in absolute values will be slower than the increase of the CR import from the PRC.

scholarly journals A Comparison of Three Temporal Smoothing Algorithms to Improve Land Cover Classification: A Case Study from NEPAL

2020 ◽  
Vol 12 (18) ◽  
pp. 2888 ◽  
Author(s):  
Nishanta Khanal ◽  
Mir Abdul Matin ◽  
Kabir Uddin ◽  
Ate Poortinga ◽  
Farrukh Chishtie ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Input Image ◽  
Series Data ◽  
Data Set ◽  
Temporal Smoothing ◽  
Land Cover Data ◽  
Cover Class

Time series land cover data statistics often fluctuate abruptly due to seasonal impact and other noise in the input image. Temporal smoothing techniques are used to reduce the noise in time series data used in land cover mapping. The effects of smoothing may vary based on the smoothing method and land cover category. In this study, we compared the performance of Fourier transformation smoothing, Whittaker smoother and Linear-Fit averaging smoother on Landsat 5, 7 and 8 based yearly composites to classify land cover in Province No. 1 of Nepal. The performance of each smoother was tested based on whether it was applied on image composites or on land cover primitives generated using the random forest machine learning method. The land cover data used in the study was from the years 2000 to 2018. Probability distribution was examined to check the quality of primitives and accuracy of the final land cover maps were accessed. The best results were found for the Whittaker smoothing for stable classes and Fourier smoothing for other classes. The results also show that classification using a properly selected smoothing algorithm outperforms a classification based on its unsmoothed data set. The final land cover generated by combining the best results obtained from different smoothing approaches increased our overall land cover map accuracy from 79.18% to 83.44%. This study shows that smoothing can result in a substantial increase in the quality of the results and that the smoothing approach should be carefully considered for each land cover class.

The Future of Liberal Democracies, Pandemic Trade-Offs, and the Role of Social Sciences

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Radoslaw Markowski
Keyword(s):  
Social Sciences ◽  
Previous Knowledge ◽  
Liberal Democracies ◽  
Trade Off ◽  
Trade Offs ◽  
Norms And Values ◽  
The Future ◽  
Democratic Norms

The article is written in the form of an essay (for Dahrendorf Symposium), speculative in essence, yet based on the new selected evidence concerning peoples’ opinions and attitudes disclosed during the pandemic. It starts with remarks about predictions in social sciences and the complex problems in studying the shocks created by the Covid-19 pandemic. Second part is devoted to major challenges and trade-offs states, governments and citizens have to face currently, focusing on one particular which is crucial for the future quality of liberal democracies, that is a trade-off between democratic norms and values and surveillance practices. The article concludes with a discussion of several issues, which have become more salient during the pandemic, challenging our previous knowledge about them.

How Did We Do?

2020 ◽  
pp. 171-185
Author(s):  
Elizabeth A. McCune ◽  
Sarah R. Johnson
Keyword(s):  
Benchmark Data ◽  
Trade Offs ◽  
The Future

The purpose of this chapter is to provide an overview of survey benchmark data, including where and how to access external survey benchmarks, what to consider when evaluating survey benchmarks, and a glimpse into the future of survey benchmarks. Practitioners are encouraged to evaluate the quality of benchmarks by considering both the generalizability of the sample used to generate the benchmarks as well as the comparability and relevance of benchmark items. Practitioners are also encouraged to consider how benchmarks can be used to drive action, where they might provide the most useful context, and the compromises and trade-offs required in the use of benchmarks.

Quality of Life, Physical Activity, and Built Environment Characteristics Among Colombian Adults

2010 ◽  
Vol 7 (s2) ◽  
pp. S181-S195 ◽  
Author(s):  
Olga L. Sarmiento ◽  
Thomas L. Schmid ◽  
Diana C. Parra ◽  
Adriana Díaz-del-Castillo ◽  
Luis Fernando Gómez ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Physical Activity ◽  
Health Status ◽  
Built Environment ◽  
Mass Transit ◽  
Urban Setting ◽  
World Health ◽  
Cross Sectional ◽  
The Future

Background:Studies assessing the association between health-related quality of life (HR-QOL) with physical activity (PA) and built environment (BE) characteristics are limited.Methods:A cross-sectional study was conducted among 1,334 adults from Bogotá, to assess the associations between HR-QOL with PA and BE characteristics. HR-QOL was measured using the World Health Organization and the Centers for Disease Control and Prevention instruments. PA was measured using the International PA Questionnaire. BE characteristics included the dimensions of density, diversity, design, and access to mass-transit. Analysis included multilevel modeling.Results:Adults who reported meeting PA recommendations and participating in the Ciclovía were more likely to have a high mean score of HR-QOL and were more likely to perceive their health status as good/excellent. Adults who reported biking for transportation were more likely to have a high mean score of HR-QOL. Regarding BE characteristics, land-use heterogeneity was associated with HR-QOL, perceived good health status and being positive about the future. Park density was associated with HR-QOL, perceived health status good/excellent and being positive about the future. Mass-transit stations availability was negatively associated with HR-QOL.Conclusion:This study provides preliminary evidence that HR-QOL is associated with PA and BE characteristics among adults in an urban setting of the developing world.

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