Team: 66

School: Socorro High

Area of Science: Computer Science, Behavioral and Social Sciences


Definition of the Problem

In the modern era, crime is a problem that many municipalities face. In New Mexico, the rate of violent crimes in 2018 was 856.6 per 100,000 people and the rate of property crimes was 3,419.7 per 100,000 people [1]. While property crime is falling, violent crime is on the rise. The obvious solution to combat rising crime is to deploy more law enforcement officers in cities, but that means higher spending on training, equipment, and paying the officer which will cost more in the long run. The bureaucracy behind acquiring the proper funding for expanding the police force is also strenuous and takes a lot of time. A viable solution, then, may be to deploy the limited amount of officers an agency has to locations where future, priority one crimes are predicted to occur. To this end, our team has devised a plan to map and predict crimes based on current data and use these predictions to determine which locations would be best to place an officer based on proximity to the predicted crime, response time, and the density of crimes in the area.

Solution to the Problem

Our team has devised a solution that involves mapping and forecasting crime, and then using a computational model to determine the best place to deploy an officer. The forecasting model will use current crime data to place future crimes at their most probable location. Using this data, the program will determine where to place a law enforcement officer based on proximity to future crimes, how many future and current crimes in an area, the severity of said crimes, and how many officers are available. With efficient placement that is near the likeliest sites of crime, response time will hasten as officers have to travel less to the scene of a crime.

Current Progress

Our team is currently making progress on a heat map to display crimes, written in JavaScript [4] and PHP [5], based on the Google API [2] as well as developing a system to predict crimes based on past data, written in python. The heat map will map both committed crimes and predicted crimes as well as officer stationing suggestions. The crime prediction system currently works by taking the average of a linear regression line with later points being weighted more heavily and a regression line of the recent trend, generated from evaluating the data of a region. [3]

Expected Results

We expect that with efficient and calculated deployment of law enforcement officers, response times will hasten which in turn improves public safety. Municipalities would also spend less money on hiring new officers as the placement of these officers are at their most optimal deployment area.


[1] Crime,

[2] Google Maps Platform  |  Google Developers, Google,

[3] “""”,

[4] “HTML.” W3Schools Online Web Tutorials,

[5] “Documentation.” Php,

Team Members:

  Elias Zheng
  Rio Sessions
  Cody Johnston
  Lucas Ward

Sponsoring Teacher: Jay Garcia

Mail the entire Team