Police Response Time and Injury Outcomes

The delayed response of law enforcement to calls for service has become a hot button issue when evaluating police department performance. While it is often assumed that faster response times could play an important role in quelling potentially violent incidents, there is no empirical evidence to support this claim. In this paper, we measure the effect of police response time on the likelihood that an incident will result in an injury. To overcome the endogeneity of more severe calls being assigned higher priority, which requires a faster response, we take several steps. First, we focus on the subset of calls for service categorized as ‘Major Disturbance—Violence’ that all receive the same priority level. Second, we instrument for police response time with the number of vehicles within a 2.5-mile radius of the incident at the time it is received by the call center. When controlling for beat, month, and time-of-day fixed effects, this instrumenting strategy allows us to take advantage of the geographical constraints faced by a dispatcher when assigning officers to an incident. In contrast to the OLS estimates, our two-stage least squares analysis establishes a strong causal relationship whereby increasing response time increases the likelihood that an incident results in an injury. The effect is concentrated among female victims, suggesting that faster response time could potentially play an important role in reducing injuries related to domestic violence

How average is average? : Temporal patterns and variability in mobile phone data

Mobile phone data – with file sizes scaling into terabytes – easily overwhelm the computational capacity available to some researchers. Moreover, for ethical reasons, data access is often granted only to particular subsets, restricting analyses to cover single days, weeks, or geographical areas. Consequently, it is frequently impossible to set a particular analysis or event in its context and know how typical it is, compared to other days, weeks or months. This is important for academic referees questioning research on mobile phone data and for the analysts in deciding how to sample, how much data to process, and which events are anomalous. All these issues require an understanding of variability in Big Data to answer the question of how average is average? This paper provides a method, using a large mobile phone dataset, to answer these basic but necessary questions. We show that file size is a robust proxy for the activity level of phone users by profiling the temporal variability of the data at an hourly, daily and monthly level. We then apply time-series analysis to isolate temporal periodicity. Finally, we discuss confidence limits to anomalous events in the data. We recommend an analytical approach to mobile phone data selection which suggests that ideally data should be sampled across days, across working weeks, and across the year, to obtain a representative average. However, where this is impossible, the temporal variability is such that specific weekdays’ data can provide a fair picture of other days in their general structure.

How average is average? : Temporal patterns and variability in mobile phone data

Mobile phone data – with file sizes scaling into terabytes – easily overwhelm the computational capacity available to some researchers. Moreover, for ethical reasons, data access is often granted only to particular subsets, restricting analyses to cover single days, weeks, or geographical areas. Consequently, it is frequently impossible to set a particular analysis or event in its context and know how typical it is, compared to other days, weeks or months. This is important for academic referees questioning research on mobile phone data and for the analysts in deciding how to sample, how much data to process, and which events are anomalous. All these issues require an understanding of variability in Big Data to answer the question of how average is average? This paper provides a method, using a large mobile phone dataset, to answer these basic but necessary questions. We show that file size is a robust proxy for the activity level of phone users by profiling the temporal variability of the data at an hourly, daily and monthly level. We then apply time-series analysis to isolate temporal periodicity. Finally, we discuss confidence limits to anomalous events in the data. We recommend an analytical approach to mobile phone data selection which suggests that ideally data should be sampled across days, across working weeks, and across the year, to obtain a representative average. However, where this is impossible, the temporal variability is such that specific weekdays’ data can provide a fair picture of other days in their general structure.

From the Guest Editors : Happy and Healthy Cities

Cities in the 21st century are magnets for people and business [...

What You See is Where You Go : Cruise Tourists’ Spatial Consumption of Destination Amenities

Using tracking technologies to measure revealed preferences can help detect locations with potential for further expansion or with risks of tourism overgrowth and consequential externalities. Understanding consumer behavior in spatio-temporal dimensions can reveal what contextual factors influence the consumption of a destination. This paper aims to contribute to knowledge on behavior-based segmentation by disaggregating spatial behavior of tourists in an intra-destination context. Behaviors were explored focusing on cruise tourists in Visby using GPS loggers and a gridded sighting experience dataset. To identify points of interest, tourists’ indicated their liking using GPS click-loggers. The results were compared to the spatial distribution of visible amenities and through a stepwise method, behavior-based segments grounded in movements and positive emotions were derived. The paper contributes to previous research on intra-destination tourist mobility by developing a method for identifying revealed behavior, and developing segments that can be used to match tourist interests to distribution of amenities. The method aims to provide stakeholders with tools that can facilitate their strategic management and marketing of a destination

Did liberal lockdown policies change spatial behaviour in Sweden? Mapping daily mobilities in Stockholm using mobile phone data during COVID-19

Sweden had the most liberal lockdown policies in Europe during the Covid-19 pandemic. Relying on individual responsibility and behavioural nudges, their effectiveness was questioned from the perspective of others who responded with legal restrictions on behaviour. In this study, using mobile phone data, we therefore examine daily spatial mobilities in Stockholm to understand how they changed during the pandemic from their pre-pandemic baseline given this background. The analysis demonstrates: that mobilities did indeed change but with some variations according to (a) the residential social composition of places and (b) their locations within the city; that the changes were long lasting; and that the average fall in spatial mobility across the whole was not caused by everybody moving less but instead by more people joining the group of those who stayed close to home. It showed, furthermore, that there were seasonal differences in spatial behaviour as well as those associated with major religious or national festivals. The analysis indicates the value of mobile phone data for spatially fine-grained mobility research but also shows its weaknesses, namely the lack of personal information on important covariates such as age, gender, and education