Estimating the Spatial Distribution of Crime Events around a Football Stadium from Georeferenced Tweets

Abstract

Abstract:Crowd-based events, such as football matches, are considered generators of crime. Criminological research on the influence of football matches has consistently uncovered differences in spatial crime patterns, particularly in the areas around stadia. At the same time, social media data mining research on football matches shows a high volume of data created during football events. This study seeks to build on these two research streams by exploring the spatial relationship between crime events and nearby Twitter activity around a football stadium, and estimating the possible influence of tweets for explaining the presence or absence of crime in the area around a football stadium on match days. Aggregated hourly crime data and geotagged tweets for the same area around the stadium are analysed using exploratory and inferential methods. Spatial clustering, spatial statistics, text mining as well as a hurdle negative binomial logistic regression for spatiotemporal explanations are utilized in our analysis. Findings indicate a statistically significant spatial relationship between three crime types (criminal damage, theft and handling, and violence against the person) and tweet patterns, and that such a relationship can be used to explain future incidents of crime.

Revisiting the Role of Place in Geographic Information Science

“…The transition between GIScience research and other scholarly work becomes more vague; as such, the more semantic modeling is emphasized. Many applications use the output of latent semantic analysis (LSA) from a-typically online-text corpus and utilize semantic, semiotic, and linguistic analysis (e.g., [91][92][93][94]). In fact, those methods are increasingly used to gather information on the mood, opinion, and emotional responses of individuals in a variety of contexts, namely space, time, and situation-specific (only applicable for this situation) context (e.g., [77,95,96]).…”

“…Various recent studies using social media, particularly Twitter data, aim to reveal a sense of place. While many computer scientists and computer linguists, on the other hand, discover the power of spatial concepts [95,[97][98][99][100][101][102][103], and likewise, GIScientists discover the potential of semantic analyses [77,94,96,[104][105][106], the dividing line between spatial and non-spatial applications diminishes.…”

Types of Crime, Poverty, Population Density and Presence of Police in the Metropolitan District of Quito

“…To further the present research, an updated UBN index can be calculated, using information from 2020 Population and Housing Census, and the collection of updated crime data can be deferred to future work. Additionally, not only police stations and population can be understood in relation to crime patterns, but also several elements of the built environment such as schools, hospitals or sports location [62,63].…”

Privacy Threats and Protection Recommendations for the Use of Geosocial Network Data in Research

“…In recent years, data from geosocial networks such as Twitter, Flickr, Instagram, Foursquare and others have become a comprehensively used basis for geospatial analysis in a number of application areas, including disaster management (Laituri and Kodrich 2008;Resch et al 2018), public health and epidemiology (Santillana et al 2015;Boulos et al 2011), urban planning (Foth et al 2011;Resch et al 2016), traffic management (Pan et al 2013;Steiger et al 2016a), crime analysis (Malleson and Andresen 2015;Ristea et al 2018;, and others. While early research efforts focused on simple analysis using traditional methods (Girardin et al 2008;Sagl et al 2012), more recent research has developed more sophisticated approaches, including self-learning systems such as artificial neural networks (ANN) (Steiger et al 2016b), machine learning semantic topic models (Hasan and Ukkusuri 2014;Kovacs-Gyori et al 2018) or real-time analysis algorithms (Sakaki et al 2010).…”