Analyzing the relationship between air pollution and various types of crime

High ambient temperatures are associated with urban crime risk in Chicago

Urban crime (UC) seriously affects the security and stability of the communities and society. However, the effects of external temperatures on the risk of UC are still confusing. We quantitatively estimated the effects of high and low temperatures on UC in Chicago. After controlling for the confounding factors, we found that high temperature has a positive promoting effect on UC, for non-domestic crime, the effect occurs at lag day 0 with a maximum risk of 1.40 (95%CI, 1.34-1.46) compared to a risk of 1 at temperature of -12.3 °C, and decreased as the lag day increased. The effect of low temperature is not significant for UC. Heat waves above the 99th percentile with a duration of 4.5-5.5 days exert a significant positive impact on non-domestic crime of UC. Our findings confirm the adverse promotion effect of high temperature on UC risk, and effective individual behavior guidance and administrative intervention are of great significance for reducing the risk of UC under specific high temperature environment.

A tested method for assessing and predicting weather-crime associations

Few studies have focused on haze as a weather element and its correlation with crime. In this study, we examined haze as a weather variable to investigate its effects on criminal activity. We used both monthly crime data and weather records to build a regression model that contains a sequential statistical approach to reach the correlation coefficients between the variables. Also, we developed a prediction model to predict crime cases considering three weather factors: temperature, humidity, and haze. We applied this model in two different climate provinces in Saudi Arabia, namely, Riyadh and Makkah. Riyadh is a desert area and observes haze approximately 17 days per month on average, while Makkah is a coastal area observing haze an average of 4 days per month. We found a measurable relationship between each of these three variables and criminal activity. We found that a one-degree increase in temperature was associated with an increase in assault of 0.739, when humidity and haze were held constant. For other independent variables measured against the same crime in Riyadh, a one-degree increase in humidity was associated with a 0.164 increase in assault. An increase in the number of times that a haze phenomenon is observed was associated with an increase of 0.359 in assault cases. Haze had the most effect on theft, drug, and assault crimes in Riyadh compared to the other elements. Temperature and humidity have a significant relationship with crime in Makkah, while haze had no significant influence in that region.

Spatio-Temporal Evolution and Spatial Heterogeneity of Influencing Factors of SO2 Emissions in Chinese Cities: Fresh Evidence from MGWR

In this study, based on the multi-source nature and humanities data of 270 Chinese cities from 2007 to2018, the spatio-temporal evolution characteristics of SO2 emissions are revealed by using Moran’s I, a hot spot analysis, kernel density, and standard deviation ellipse models. The spatial scale heterogeneity of influencing factors is explored by using the multiscale geographically weighted regression model to make the regression results more accurate and reliable. The results show that (1) SO2 emissions showed spatial clustering characteristics during the study period, decreased by 85.12% through pollution governance, and exhibited spatial heterogeneity of differentiation. (2) The spatial distribution direction of SO2 emissions’ standard deviation ellipse in cities was “northeast–southwest”. The gravity center of the SO2 emissions shifted to the northeast, from Zhumadian City to Zhoukou City in Henan Province. The results of hot spots showed a polarization trend of “clustering hot spots in the north and dispersing cold spots in the south”. (3) The MGWR model is more accurate than the OLS and classical GWR regressions. The different spatial bandwidths have a different effect on the identification of influencing factors. There were several main influencing factors on urban SO2 emissions: the regional innovation and entrepreneurship level, government intervention, and urban precipitation; important factors: population intensity, financial development, and foreign direct investment; secondary factors: industrial structure upgrading and road construction. Based on the above conclusions, this paper explores the spatial heterogeneity of urban SO2 emissions and their influencing factors, and provides empirical evidence and reference for the precise management of SO2 emission reduction in “one city, one policy”.