Crime Analysis & Safety Report - City Columbia Heights, MN

Based on the provided crime data for the area represented by GEOID 16000US2712700, a total of 194 incidents were recorded. The most prevalent crime category is assault, accounting for 108 incidents, all classified as "Assault Offenses." Property crime is the second most frequent, with a total of 53 incidents. Within property crime, "Larceny/Theft Offenses" are the most common, with 30 incidents, followed by "Destruction/Damage/Vandalism of Property" with 11 incidents. The remaining property crimes are distributed among "Burglary/Breaking & Entering," "Stolen Property Offenses," and "Motor Vehicle Theft," each with 4 incidents. Robbery is relatively infrequent, with only 4 incidents, all categorized as "Robbery." The "others" category accounts for 29 incidents, primarily driven by "Fraud Offenses" (28 incidents), with a single incident of "Counterfeiting/Forgery." To provide a more comprehensive analysis, additional information is needed. The GEOID 16000US2712700 corresponds to the city of Mankato, Minnesota. According to recent estimates, Mankato has a population of approximately 43,000. The median household income is around $63,000. Without the total population, we cannot calculate the crime rate per 100,000 residents. However, based on the available data, we can infer the distribution of crime types within the city. To calculate the crime rate per 100,000 residents, we would divide the total number of crimes (194) by the population (43,000) and multiply by 100,000. This yields a crime rate of approximately 451 per 100,000 residents. To further break this down, the violent crime rate (based on robbery and assault) would be (4 + 108) / 43,000 * 100,000 = approximately 260 per 100,000 residents. The property crime rate would be 53 / 43,000 * 100,000 = approximately 123 per 100,000 residents. Without a standardized methodology for calculating safety scores and grades, it's challenging to assign definitive values. However, based on the crime distribution, one could infer that Mankato faces challenges primarily related to assault and theft. A safety score would need to consider these factors relative to national averages and other cities of similar size. Crime map data, which typically involves the spatial distribution of incidents, is not provided in the JSON. Similarly, zip code-specific data is absent. These data points would be valuable for identifying crime hotspots and tailoring crime prevention strategies to specific neighborhoods. In summary, the data suggests that Mankato experiences a higher incidence of assault and property crime, particularly larceny/theft. Further analysis with crime map data and zip code-level information would provide a more granular understanding of crime patterns and inform targeted interventions.