Previous literature has examined the relationship between the amount of green space and perceived safety in urban areas, but little is known about the effect of street-view neighborhood greenery on perceived neighborhood safety. Using a deep learning approach, we derived greenery from a massive set of street view images in central Guangzhou. We further tested the relationships and mechanisms between street-view greenery and fear of crime in the neighborhood. Results demonstrated that a higher level of neighborhood street-view greenery was associated with a lower fear of crime, and its relationship was mediated by perceived physical incivilities. While increasing street greenery of the micro-environment may reduce fear of crime, this paper also suggests that social factors should be considered when designing ameliorative programs.

Geospatial social media (GSM) data has been increasingly used in public health due to its rich, timely, and accessible spatial information, particularly in infectious disease research. This review synthesized 86 research articles that use GSM data in infectious diseases published between December 2013 and March 2022. These articles cover 12 infectious disease types ranging from respiratory infectious diseases to sexually transmitted diseases with spatial levels varying from the neighborhood, county, state, and country. We categorized these studies into three major infectious disease research domains: surveillance, explanation, and prediction. With the assistance of advanced computing, statistical and spatial methods, GSM data has been widely and deeply applied to these domains, particularly in surveillance and explanation domains. We further identified four knowledge gaps in terms of contextual information use, application scopes, spatiotemporal dimension, and data limitations and proposed innovation opportunities for future research. Our findings will contribute to a better understanding of using GSM data in infectious diseases studies and provide insights into strategies for using GSM data more effectively in future research.

The literature shows that offenders’ subsequent crime location choices are affected by their prior crime location choices. However, the published studies have focused on the influence of time and place of a previous crime, without testing the impact of whether the offender was arrested during the act of the prior crime. On the basis of the literature, this study further examines the influence of the prior robbery experiences on the subsequent street robbery location choices, by testing explicit hypotheses on how the time, place, and being arrested in the act of previous robberies affect a robber’s subsequent decisions of where to commit robberies. The data set used in this study includes 1262 detected robberies committed by 527 street robbers from the ZG City Public Security Bureau in China. Results of a mixed logit model demonstrate that prior street robbery experiences have a strong effect on subsequent street robbery location choices. A shorter time interval and less possibility of being arrested in the act of a prior street robbery significantly increase the likelihood of a robber returning to the previous location. However, the impact of distance of journey to prior crime location is not statistically significant.

Fear of crime can lead to lower satisfaction with life and subjective well-being. The indicators of fear of crime vary from the social and cultural context, and the hukou (household registration) status causes unequal rights between local hukou and non-local hukou residents in China. To improve people’s perception of safety, this study takes hukou as an indicator of social vulnerability and examines the relationship between hukou, perceived neighborhood conditions, and fear of crime in China. A binary logistic regression model was used to analyze the 1727 residents garnered from the 2016 Project on Public Safety in Guangzhou Neighborhoods (PPSGN) in Guangzhou, China. The results show that women, victimization experience, physical and social disorder, and neighborhood policing are associated with residents’ fear of crime. Although hukou status has no statistically significant effect on fear of crime, hukou status significantly moderates the influence of perceived neighborhood conditions on fear of crime. That is, perceived neighborhood conditions’ effects on fear are conditional on one’s hukou status: non-local hukou, perception of the social disorder has more of the detrimental effect on fear, and perception of social integration has less of the helpful effect on fear. In sum, this study adds to the international literature by revealing the conditional effect of the hukou on fear in a Chinese city.

A tremendous amount of research use questionnaires to obtain individuals’ fear of crime and aggregate it to the neighborhood level to measure the spatial distribution of fear of crime. However, the cost of using questionnaires to measure the large-scale spatial distribution of fear of crime is high. The built environment is known to influence people’s perceptions, including fear of crime. This study develops a machine learning model to link built environment extracted from street view images to fear of crime obtained from questionnaires, and then applies this model to extrapolate fear of crime for neighborhoods without the questionnaires. Using massive street view images and a survey among 1,741 residents in 80 neighborhoods in Guangzhou, China, this study developed a novel systematic approach to measuring large-scale spatial fear of crime at the neighborhood level for 1,753 neighborhoods. This is the first study to measure fear of crime at the neighborhood level for a metropolitan area of nearly 20 million people. The integration of survey data and street view images provides an opportunity to develop a more effective way to measure the spatial distribution of fear of crime. This approach could be applied to map other types of perceptions at a spatial resolution of the neighborhood level.