We provide recommendations on the optimal location and composition of Atlanta police stations by clustering neighborhoods based on crime type, number of car crashes, and demographics data and minimizing the distance between potential police station and crime locations. By doing so, we were able to decrease the average distance between police station and crime locations for each patrol zone by 40%.

Atlanta has been increasing in crime despite strained policing resources. By optimizing police station locations, we hope to reduce crime rates and increase response efficiency. Previous research focuses on locating police stations close to crime hotspots but do not give details such as the staff composition. As different neighborhoods may have different types of crime, we aim to provide specific composition recommendations for each police station.

A total of 101 Atlanta neighborhoods were analyzed using the following datasets.

Crime Data | Crash Data | Demographics Data | |
---|---|---|---|

Duration | 2015-2019 | 2015-2019 | 2019 |

# Records | 136,175 | 204,336 | 103 |

# Columns | 10 | 22 | 20 |

Source | APD | ATL DOT | DataNexus |

We reduced the number of variables by choosing those with standard deviation > 0.01, then we used K means to cluster the neighborhoods using these factors. The elbow method was used to find the optimal K=4, then the neighborhoods of the same cluster were grouped into police zones.

We created a mixed integer linear program to minimize the distance between the potential police stations (i) to crime locations. The candidate locations were generated by taking the intersection of vertical and horizontal lines plotted one mile apart from each other over each zone. The crime locations were divided into violent (j) and non-violent crime types (k), and a weight of 0.7 and 0.3 were given to each type, respectively, so that more importance is given to minimizing distance to violent crime type locations.

The binary variable \( x_i \) is 1 if potential location \( i \) is chosen and 0 otherwise. The parameter \( D_{H_{ij}} \) represents the distance in miles from location \( i \) to \( j \), and \( D_{L_{jk}} \) is the distance in miles from location \( j \) to \( k \). The parameter \( n \) is the number of police stations needed for the zone.

The binary variable x_{i} is 1 if potential location i is chosen and 0
otherwise. The parameter D_{Hij} represents the distance in miles
from location i to j, and D_{Ljk} is the distance is miles from location
j to k. The parameter n is the number of police stations needed
for the zone.

The following allows interaction with the analyzed results and shows important statistics that were taken into
consideration for clustering the neighbourhoods. Unfortunately there was no data found for the airport
neighborhood; therefore, its interactivity is limited. The library used for web reactive interactions is D3.js.
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