Dead Spaces: Sport Venues and Police Stops in a Major League, Upper Midwestern City in the United States

Abstract

Attention by sport management researchers and practitioners toward the societal externalities of professional sport franchises and venues has increased recently. This study asserts that while sport organizations are very active in this regard, there remain several issues that have not received much attention in the sport management literature nor by sport organizations themselves. Criminal activity, or the perception of criminal activity, at and near sport venues is one of these issues. The negative binominal regression analysis of police stops in Minneapolis revealed that police stops were greater within a quarter and half a mile of Minneapolis professional sport venues on event days. Furthermore, during nonevent days, the venues can be urban “dead spaces” and the design of venues in urban areas should address the internal and external amenities of the sport venues and the potential increase in crime and police-related activity on days with and without events.

A Comparative Analysis of Social Media Fan (Community) Engagement in a European and a North American Pro-Sport League and Their Reaction to Industry-Wide Disruptions

“…Our second step is the regression analysis using ordinary least square (OLS) and an analysis of Poisson distribution using a negative binomial regression (NBR) estimating count data (Hutto et al, 2013). NBR models are customarily used in other count models such as crime analysis, estimating the proportional difference (between 0 and 1) in the number of "incidents" at different time periods (Chang, 2005;Garnowski & Manner, 2011;Jakar & Gordon, 2021). In our specific model, we are using the summation of daily data to examine the number of posts, number of reactions ("likes," "Retweets," "quotes," and "replies"), and the ratio of engagement to Tweets for each of the days and include both random and fixed variables to estimate how each of the periods are associated with the proportional change in each of the two dependent variables.…”