Using tweets to understand changes in the spatial crime distribution for hockey events in Vancouver

Comparative Study of Approaches for Detecting Crime Hotspots with Considering Concentration and Shape Characteristics

Hotspot detection is an important exploratory technique to identify areas with high concentrations of crime and help deploy crime-reduction resources. Although a variety of methods have been developed to detect crime hotspots, few studies have systematically evaluated the performance of various methods, especially in terms of the ability to detect complex-shaped crime hotspots. Therefore, in this study, a comparative study of hotspot detection approaches while simultaneously considering the concentration and shape characteristics was conducted. Firstly, we established a framework for quantitatively evaluating the performance of hotspot detection for cases with or without the "ground truth". Secondly, accounting for the concentration and shape characteristics of the hotspot, we additionally defined two evaluation indicators, which can be used as a supplement to existing evaluation indicators. Finally, four classical hotspot-detection methods were quantitatively compared on the synthetic and real crime data. Results show that the proposed evaluation framework and indicators can describe the size, concentration and shape characteristics of the detected hotspots, thus supporting the quantitative comparison of different methods. From the selected methods, the AMOEBA (A Multidirectional Optimal Ecotope-Based Algorithm) method was more accurate in describing the concentration and shape characteristics and was powerful in discovering complex hotspots.

Public Participation and Information Disclosure for Environmental Sustainability of 2022 Winter Olympics

As China prepares to host the 2022 Winter Olympics, the Beijing Winter Olympics Organizing Committee has committed to making public participation a fundamental part of its broader sustainability objectives. Unfortunately, the existing research on information openness and public participation towards Winter Olympic Games is limited in the perspective of host residents. Therefore, this article aims to understand the information disclosure and public participation, as well as the roles information and technologies (ICTs) play in achieving environmental sustainability. With the help of self-administered questionnaires, data were compiled. The survey was conducted in April 2017 with 650 residents in Beijing and Zhangjiakou via face-to-face interviews to obtain a random and statistically representative sample of host residents. Our findings indicated that only few respondents participated in the limited activities of Beijing 2022 Winter Olympics. The respondents’ views on important issues of public participation varied obviously including the participating level, principles, time, ways, mechanism, and influencing factor. The analysis illustrated over 70% of respondents had the positive attitudes towards ICTs roles and functions in Beijing 2022. ICTs have helped the public to obtain information about environmental management and sustainability issues, and to facilitate public awareness and collective action by building new management practices. This study has implications and contributes to the burgeoning literature surrounding the Olympic Games by providing an in-depth analysis of public participation in relation to the theory.

The Spillover Effect of Geotagged Tweets as a Measure of Ambient Population for Theft Crime

As a measurement of the residential population, the Census population ignores the mobility of the people. This weakness may be alleviated by the use of ambient population, derived from social media data such as tweets. This research aims to examine the degree in which geotagged tweets, in contrast to the Census population, can explain crime. In addition, the mobility of Twitter users suggests that tweets as the ambient population may have a spillover effect on the neighboring areas. Based on a yearlong geotagged tweets dataset, negative binomial regression models are used to test the impact of tweets derived ambient population, as well as its possible spillover effect on theft crimes. Results show: (1) Tweets count is a viable replacement of the Census population for spatial theft pattern analysis; (2) tweets count as a measure of the ambient population shows a significant spillover effect on thefts, while such spillover effect does not exist for the Census population; (3) the combination of tweets and its spatial lag outperforms the Census population in theft crime analyses. Therefore, the spillover effect of the tweets derived ambient population should be considered in future crime analyses. This finding may be applicable to other social media data as well.