Community-driven disorder reduction: Crime prevention through a clean and green initiative in a legacy city

Urban Studies ◽  
2020 ◽  
Vol 57 (14) ◽  
pp. 2956-2972
Author(s):  
Jesenia M. Pizarro ◽  
Richard C. Sadler ◽  
Jason Goldstick ◽  
Brandon Turchan ◽  
Edmund F. McGarrell ◽  
...  
Keyword(s):  
Crime Prevention ◽  
Prevention And Control ◽  
Temporal Trends ◽  
Additive Models ◽  
Level Data ◽  
Spatial Dependencies ◽  
Temporal And Spatial ◽  
And Control ◽  
Point Level ◽  
Over Time

This study examines the effects of a neighbourhood greening and beautification strategy called Clean & Green on crime prevention and reduction. Point level data for all Part I index crimes and Clean & Green efforts in the study area from 2005 to 2014 are analysed using spatial and linear regression with two key modifications: (1) controlling for temporal and spatial dependencies between points; and (2) allowing for potentially non-linear temporal trends in the effect of cumulative greening. To accommodate those modifications, generalised additive models (GAMs) were employed. The analyses of violent and property crimes suggest that greening efforts are increasingly protective over time. The findings demonstrate that the elimination of blight and disorder via neighbourhood greening and beautification efforts can be an effective tool for crime prevention and control in communities.

scholarly journals The haze extreme co-movements in Beijing–Tianjin–Hebei region and its extreme dependence pattern recognitions

2020 ◽  
Vol 103 (2) ◽  
pp. 003685042091631 ◽  
Author(s):  
Lu Deng ◽  
Zhengjun Zhang
Keyword(s):  
Correlation Coefficient ◽  
Early Warning System ◽  
Warning System ◽  
Time Difference ◽  
Lag Effects ◽  
Extreme Dependence ◽  
Level Data ◽  
Temporal And Spatial ◽  
And Control ◽  
Meteorological Models

Extreme haze was often observed at many locations in Beijing–Tianjin–Hebei region within several hours when they occurred, which is referred to as extreme co-movements and extreme dependence in statistics. This article applies tail quotient correlation coefficient to explore the temporal and spatial extreme dependence patterns of haze in this region. Hourly PM2.5 station-level data during 2014–2018 are used, and the results show that the tail quotient correlation coefficient between stations increases with month. Specifically, the simultaneous extreme dependence was strong in the fourth season, while the haze was severe. In the first season, while the haze was also severe, the extreme hazes only show strong co-movements with a time difference. These observations lead to the study of two special scenarios, that is, the concurrence/extreme dependence of the worst extreme haze and its lag effects. City clusters suffering simultaneous extreme haze or with certain time difference as well as the most frequently co-movement cities are identified. The extreme co-movements of these cities and the reasons for their occurrences have strong implications for improving the PM2.5 joint prevention and control in the Beijing–Tianjin–Hebei region. The importance of lag effects is also reflected in the precedence order of the extreme haze’s appearance. It is especially useful when setting the mechanism of the early warning system which can be triggered by the first appearance of extreme haze. The precedence orders also avail in investigating the transmission path of the haze, based on which more precise meteorological models can be made to benefit the haze forecasting of the region.

scholarly journals Community participation in crime prevention and control in rural Nigeria

Heliyon ◽  
2020 ◽  
Vol 6 (9) ◽  
pp. e05015
Author(s):  
Ogadimma Arisukwu ◽  
Chisaa Igbolekwu ◽  
Joseph Oye ◽  
Eyitayo Oyeyipo ◽  
Festus Asamu ◽  
...  
Keyword(s):  
Community Participation ◽  
Crime Prevention ◽  
Prevention And Control ◽  
Rural Nigeria ◽  
And Control

scholarly journals Predictive study of tuberculosis incidence by time series method and Elman neural network in Kashgar, China

BMJ Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. e041040
Author(s):  
Yanling Zheng ◽  
Xueliang Zhang ◽  
Xijiang Wang ◽  
Kai Wang ◽  
Yan Cui
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Hybrid Method ◽  
Prevention And Control ◽  
Temporal Trends ◽  
Percentage Error ◽  
Elman Neural Network ◽  
Prediction Analysis ◽  
And Control

ObjectivesKashgar, located in Xinjiang, China has a high incidence of tuberculosis (TB) making prevention and control extremely difficult. In addition, there have been very few prediction studies on TB incidence here. We; therefore, considered it a high priority to do prediction analysis of TB incidence in Kashgar, and so provide a scientific reference for eventual prevention and control.DesignTime series study.Setting Kashgar, ChinaKashgar, China.MethodsWe used a single Box-Jenkins method and a Box-Jenkins and Elman neural network (ElmanNN) hybrid method to do prediction analysis of TB incidence in Kashgar. Root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to measure the prediction accuracy.ResultsAfter careful analysis, the single autoregression (AR) (1, 2, 8) model and the AR (1, 2, 8)-ElmanNN (AR-Elman) hybrid model were established, and the optimal neurons value of the AR-Elman hybrid model is 6. In the fitting dataset, the RMSE, MAE and MAPE were 6.15, 4.33 and 0.2858, respectively, for the AR (1, 2, 8) model, and 3.78, 3.38 and 0.1837, respectively, for the AR-Elman hybrid model. In the forecasting dataset, the RMSE, MAE and MAPE were 10.88, 8.75 and 0.2029, respectively, for the AR (1, 2, 8) model, and 8.86, 7.29 and 0.2006, respectively, for the AR-Elman hybrid model.ConclusionsBoth the single AR (1, 2, 8) model and the AR-Elman model could be used to predict the TB incidence in Kashgar, but the modelling and validation scale-dependent measures (RMSE, MAE and MAPE) in the AR (1, 2, 8) model were inferior to those in the AR-Elman hybrid model, which indicated that the AR-Elman hybrid model was better than the AR (1, 2, 8) model. The Box-Jenkins and ElmanNN hybrid method therefore can be highlighted in predicting the temporal trends of TB incidence in Kashgar, which may act as the potential for far-reaching implications for prevention and control of TB.

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