spatiotemporal characteristics
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PsyCh Journal ◽  
2022 ◽  
Author(s):  
Weizhi Nan ◽  
Yuqi Liu ◽  
Xianqing Zeng ◽  
Weibin Yang ◽  
Junhua Liang ◽  
...  

Author(s):  
Josephine Kirui ◽  
Joshua Ngaina ◽  
Nzioka John Muthama ◽  
Gachuiri Charles Karuku

Milk production in Kenya is predominantly smallholder and dependent on rainfall. The study assesses spatiotemporal characteristics of smallholder milk production in Nandi County under changing climate. Climate (Rainfall and temperature), fodder availability (Normalized Difference Vegetation Index (NDVI) and soil moisture content) and milk production data were used. Methods included trend analysis, spatial plots, correlation and multi-regression analysis. Monthly NDVI and soil moisture content were high between April and November with seasonal analysis indicating highest/lowest June-August (JJA)/December-February (DJF) values. Percentage change (%Δ) for NDVI was 6.0% (DJF), 1.96% (March-May, MAM), 2.13% (JJA), 4.16% (September-November, SON) and (2.53% (Annual). Seasonal and annual %Δ for soil moisture content ranged 7.2-17.1% at 0-10cm level and 8.1-23.7% at 10-40 level. Trend analysis of milk production showed positive change from 2007 to 2016 and highest/lowest in December/April with seasonal %Δ of up to 186% (MAM), 183% (JJA), 202% (SON), 214% (DJF) and 204% (Annual). Majority of household (HH) owned between 1 and 20 acres of land with only 0.5 to 2 acres allocated to dairy farming while those allocating less than 1 acre practiced zero grazing. On average, HH had 2 lactating cows throughout the year with majority of dairy farmers (98.6%) owning improved cow breeds. Amount of milk per HH supplied to the farmer organization varied between 2.3 litres and 3.8 litres with computed daily average milk produced per HH being 18.8 litres. Active milk suppliers were highest/lowest in December/April whereas daily average milk production per HH between 2010 and 2016 was highest/lowest in January (23.7 litres)/August (15.6 litres). Lowest/highest correlation coefficients were found in precipitation/minimum temperature. Multi-regression analysis indicated that precipitation had significant contribution to dairy productivity. Given the sensitivity of milk production to climate and fodder availability, adequate adaptation and mitigation measures are necessary in order to sustainably enhance milk production.


2021 ◽  
Vol 50 (1) ◽  
pp. 366-366
Author(s):  
Xiaofeng Jia ◽  
Zhuoran Wang ◽  
Songyu Chen ◽  
Mark Smith

2021 ◽  
Vol 11 (24) ◽  
pp. 11750
Author(s):  
Hongbo Li ◽  
Jincheng Wang ◽  
Yilong Ren ◽  
Feng Mao

Traffic prediction is a critical aspect of many real-world scenarios that requires accurate traffic status predictions, such as travel demand prediction. The emergence of online car-hailing activities has given people greater mobility and makes intercity travel more frequent. The increase in online car-hailing demand has often led to a supply–demand imbalance where there is a mismatch between the immediate availability of car-hailing services and the number of passengers in certain areas. Accurate prediction of online car-hailing demand promotes efficiencies and minimizes resources and time waste. However, many prior related studies often fail to fully utilize spatiotemporal characteristics. With the development of newer deep-learning models, this paper aims to solve online car-hailing problems with an ST-transformer model. The spatiotemporal characteristics of online car-hailing data are analyzed and extracted. The study region is divided into subareas, and the demand for each subarea is summed at a specific time interval. Historical demand of the areas is used to predict future demand. The results of the ST-transformer outperformed other baseline models, namely, VAR, SVR, LSTM, LSTNet, and transformers. The validated results suggest that the ST-transformer is more capable of capturing spatiotemporal characteristics compared to the other models. Additionally, compared to others, the model is less affected by data sparsity.


2021 ◽  
Vol 4 ◽  
pp. 1-5
Author(s):  
Hui Zhang ◽  
Chenyu Zuo ◽  
Linfang Ding

Abstract. Spatiotemporal distribution of the epidemic data plays an important role in its understanding and prediction. In order to understand the transmission patterns of infectious diseases in a more intuitive way, many works applied various visualizations to show the epidemic datasets. However, most of them focus on visualizing the epidemic information at the overall level such as the confirmed counts each country, while spending less effort on powering user to effectively understand and reason the very large and complex epidemic datasets through flexible interactions. In this paper, the authors proposed a novel map-based dashboard for visualizing and analyzing spatiotemporal clustering patterns and transmission chains of epidemic data. We used 102 confirmed cases officially reported by the Ministry of Health in Singapore as the test dataset. This experiment shown that the well-designed and interactive map-based dashboard is effective in shorten the time that users required to mine the spatiotemporal characteristics and transmission chains behind the textual and numerical epidemic data.


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