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With the increase of urbanization in cities, there is also the rise of crime incidence. It is important for the police force to stay ahead in proactive policing. In this paper, the researcher presents a simple approach to predict crime in a geographic space using grid thematic mapping and neural networks. The study particularly focuses on the possibility of using historical crime data in predicting future crime incidents. The dataset is divided into monthly, weekly, and daily data called as a snapshot for training the model. The study area, Cebu City, is divided into a square grid of various sizes and crime incidents, from each cell in the grid of each snapshot, will then be recorded. This data is then fed into the neural network to predict possible areas where crime would happen for the next time interval. Initial results have shown that the model is accurate enough to predict crime areas when the data snapshot is divided monthly and weekly and grid size is set to a large size of about 1000 by 1000 meters and 750 by 750 meters. The best model was able to yield an F1 score of 0.95. Although the created model is simple, this can be a stepping stone to further crime modelling and prediction studies. This tool can be used to aid decisions and policy-making in the deployment of police resources throughout the city.