Impact of human mobility on police allocation

Abstract

Motivated by recent findings that human mobility is proxy for crime behavior in big cities and that there is a superlinear relationship between the people's movement and crime, this article aims to evaluate the impact of how these findings influence police allocation. More precisely, we shed light on the differences between an allocation strategy, in which the resources are distributed by clusters of floating population, and conventional allocation strategies, in which the police resources are distributed by an Administrative Area (typically based on resident population). We observed a substantial difference in the distributions of police resources allocated following these strategies, what evidences the imprecision of conventional police allocation methods.

Traveling heterogeneity in public transportation

“…Urban mobility and public transport systems have been extensively studied over the years [14][15][16][24][25][26][27][28], however, even considering such vast literature, there is still no consensus on how to describe in the best way the efficiency of vehicle routes in large metropolises [29]. Here we propose that the level of heterogeneity of the distribution of time during the trajectory of a bus can be a measurement of the overall quality of the trip.…”

“…The data were obtained through the Fortaleza's city hall and refer to the period between the 12th and the 17th of April 2016, from Tuesday to Sunday. These data have been noteworthy used in several studies in the last few years [12][13][14][15][16]. The first is the largest dataset of the two and consists of about 21M GPS points.…”

Towards Understanding the Impact of Crime on the Choice of Route by a Bus Passenger

“…In previous studies, we have studied the impact of the movement of people on the occurrence of crime [17] as well as on police allocation strategies [36]. In this article we will follow a different strategy, as we will investigate the impact that crimes distributed in the city can have on the choice of bus routes made by people.…”

A Temporal Clustering Algorithm for Achieving the Trade-off between the User Experience and the Equipment Economy in the Context of IoT

“…We propose herein the Temporal Clustering Algorithm (TCA), an algorithm that factors multiple time series in a set of non-overlapping segments, known as time clusters. The agglomerative behavior of the TCA is inspired by the City Clustering Algorithm (CCA) [9,10] a spatial agglomeration algorithm widely used in defining cities beyond their boundaries [11][12][13][14]. The main feature of the TCA is to consider user preference through its three modes of Comfort, Balance, and Eco.…”

“…TEMPORAL CLUSTERING ALGORITHM Data: series, period, mode 1 begin i } = mean of the values of each index, i, in series 4 if mode == "Comfort" then * = µ − σ /2, where µ and σ are the mean and standard deviation of D, respectively 8 else if mode == "Eco" then * = µ, where µ is the mean of the values of D 10for ← period to size(series) BY period do11 while there exist elements in D with D i > D * do12 if reaches some event with D i > D * then13 shift to the period in which the last clustered event occurs…”