scholarly journals Phenological Classification of the United States: A Geographic Framework for Extending Multi-Sensor Time-Series Data

2010 ◽  
Vol 2 (2) ◽  
pp. 526-544 ◽  
Author(s):  
Yingxin Gu ◽  
Jesslyn Brown ◽  
Tomoaki Miura ◽  
Willem J. Van Leeuwen ◽  
Bradley Reed
Keyword(s):  
United States ◽  
Time Series ◽  
Time Series Data ◽  
The United States ◽  
Series Data

Age, Generations, and Participation

2017 ◽  
Author(s):  
Russell J. Dalton
Keyword(s):  
United States ◽  
Time Series ◽  
Life Cycle ◽  
Millennial Generation ◽  
Time Series Data ◽  
Direct Action ◽  
The United States ◽  
Series Data ◽  
Online Participation ◽  
Modes Of Action

Political scientists debate whether the Millennial generation is disengaging from politics in contemporary democracies. The ISSP surveys show that the generational decline in participation is largely limited to voting and other forms of partisan activity. At the same time, younger citizens are often more engaged in non-electoral activities, such as direct action, protest, and online participation. Time-series data for the United States disentangles the effects of life-cycle changes and generations. More recent generations display a clear decline in voting across the 1967–2014 period. In contrast, life-cycle increases in participation are more common for non-electoral activity. Both factors influence participation but in contrasting ways for different modes of action.

Crime and the Use of Prisons in the United States: A Time Series Analysis

1981 ◽  
Vol 27 (2) ◽  
pp. 206-212 ◽  
Author(s):  
Lee H. Bowker
Keyword(s):  
United States ◽  
Time Series ◽  
Time Series Analysis ◽  
Recent Article ◽  
Positive Relationship ◽  
Time Series Data ◽  
The United States ◽  
Series Data ◽  
Cross Sectional ◽  
Correctional Policy

A recent article by David Biles reported a positive relationship between crime and imprisonment, using cross-sectional data from the United States, Australia, and Canada. This article extends his analysis, using two sets of time series data on crime and imprisonment rates for the United States as a whole. The unlagged correlations between the crime and imprisonment rates for 1941-57 and 1958-78 are not statistically signifi cant, but one of six lagged correlations from 1958-78 is significant, as are four of six from 1941-57. The inconsistency in correlation provides little guidance for the development of correctional policy. Considering these findings, William Nagel's support for a moratorium on prison construction takes on the color of a reasonable, and perhaps even conservative, reading of available policy and management data rather than a radical proposition for change.

scholarly journals A Multiscale Spatial Dataset for Policy-Driven Land Developability across the United States, 2001–2011

2020 ◽  
Author(s):  
Hung Chak Ho ◽  
Guangqing Chi
Keyword(s):  
United States ◽  
Time Series ◽  
Population Dynamics ◽  
Health Risks ◽  
Time Series Data ◽  
Land Development ◽  
The United States ◽  
Land Policy ◽  
Series Data ◽  
Spatial Dataset

Abstract. Land vulnerability and development can be restricted by both land policy and geophysical limits. Land vulnerability and development cannot be simply quantified by land cover/use change, because growth related to population dynamics is not horizontal. Particularly, time-series data with a higher flexibility considering the ability of land to be developed should be used to identify areas of spatiotemporal change. By considering the policy aspects of land development, this approach will allow one to further identify the lands facing population stress, socioeconomic burdens, and health risks. Here the concept of “land developability” is expanded to include policy-driven factors and land vulnerability to better reconcile developability with socio-environmental justice. The first phrase of policy-driven land developability mapping is implemented in estimating land information across the contiguous United States in 2001, 2006, and 2011. Multiscale data products for state-, county- and census-tract-levels are provided from this estimation. The extension of this approach can be applied to other countries with modifications for their specific scenarios. The data generated from this work are available at https://doi.org/10.7910/DVN/AMZMWH (Chi and Ho, 2019).

Corn and Soybean Mapping in the United States Using MODIS Time-Series Data Sets

2007 ◽  
Vol 99 (6) ◽  
pp. 1654-1664 ◽  
Author(s):  
Jiyul Chang ◽  
Matthew C. Hansen ◽  
Kyle Pittman ◽  
Mark Carroll ◽  
Charlene DiMiceli
Keyword(s):  
United States ◽  
Time Series ◽  
Time Series Data ◽  
The United States ◽  
Series Data ◽  
Data Sets

Gasoline and Travel Demand Models Using Time Series and Cross-Section Data from United States

1996 ◽  
Vol 1558 (1) ◽  
pp. 83-89 ◽  
Author(s):  
Paul Schimek
Keyword(s):  
United States ◽  
Time Series ◽  
Travel Demand ◽  
Time Series Data ◽  
Fuel Efficiency ◽  
Rebound Effect ◽  
The United States ◽  
Series Data ◽  
Price And Income Elasticities ◽  
Demand Models

The price and income elasticities of highway gasoline and automobile travel demand are useful for forecasting gasoline tax revenues and highway investment needs and evaluating policies to reduce automobile use, improve fuel efficiency, or reduce greenhouse gas emissions. Gasoline and travel demand elasticities are calculated using 1950 to 1994 time series data for the United States and 1988 to 1992 pooled data for states of the United States. Gasoline demand was found to be price inelastic in the short run, but in the long run, it was found to be —0.7. Even in the United States, gasoline price has a significant impact on gasoline use. The response to price changes is divided among driving, fuel efficiency, and the size of the vehicle stock, although the latter is the smallest. The Corporate Average Fuel Economy (CAFE) program was found to be associated with an average 1 percent annual decline in per capita fuel consumption. The elasticity of driving with respect to fuel efficiency— the rebound effect—was found to be —0.3, confirming previous results. The state-level data produce inconclusive results; it is hypothesized that this is the result of the confounding effect of CAFE.

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

scholarly journals Time–frequency time–space LSTM for robust classification of physiological signals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Time Series ◽  
Network Architecture ◽  
Time Series Data ◽  
Short Term Memory ◽  
Physiological Signals ◽  
Series Data ◽  
Time Frequency ◽  
Time Space ◽  
Deep Recurrent Neural Network

AbstractAutomated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time–frequency and time–space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.

scholarly journals High-pass filtering artifacts in multivariate classification of neural time series data

2021 ◽  
Vol 352 ◽  
pp. 109080
Author(s):  
Joram van Driel ◽  
Christian N.L. Olivers ◽  
Johannes J. Fahrenfort
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Multivariate Classification ◽  
High Pass

scholarly journals Bill Money Classification of Japanese Yen Using Time Series Data

1995 ◽  
Vol 115 (3) ◽  
pp. 354-360 ◽  
Author(s):  
Shigeaki Fukuda ◽  
Toshihisa Kosaka ◽  
Sigeru Omatsu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Japanese Yen
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