Crime incidents, whether index or non-index may happen in any place at any time. These unlawful incidents give people a feeling of fear and uncertainty. As such, crime reporting is considered essential as it enables the monitoring of peace and order within the community. Traditionally, crime reports are sent via phone through text, or call. However, this method of crime reporting is unreliable as the salient details relevant to the crime may be inadequate or inaccurate. In this study, we present a method in crime mapping and analysis using the geo-tagged incident reports (e.g., images or videos, date, time, location, etc.) sent via mobile or web-based applications. Furthermore, we built a predictive model (i.e., using linear regression) based on the historical data that predicts crime-prone areas within the city, and consequently increase society's awareness of the likelihood of crime occurrence. Our system serves as a basis for the authorities in terms of making a decision on police visibility and which areas to prioritize in deploying electronic devices such as CCTV cameras given the limited resources. Furthermore, it paves the way for law enforcement officers to efficiently address the issue of resolving crime incidents. Experiments show that the model is efficient in terms of the predictive model's accuracy and signifies its effectiveness based on the usability and functionality tests conducted.