High Risk
per 1000 residents
The area has very low safety standards with a D rating. Residents should be aware of their surroundings and take appropriate precautions.
Given the higher rate of violent crimes (54.4% of total incidents), residents should prioritize personal safety measures, avoid walking alone at night, and stay informed about local safety alerts.
The high crime volume of 1,404 incidents indicates this is an active urban area. The most common crime type is assault.
Crime rates in Erie align with national averages, suggesting typical urban safety patterns.
Based on the provided crime data for an area identified by the GEOID 16000US4224000, a detailed analysis reveals several key trends. The total count of reported crimes is 1404. The most prevalent crime category is assault, accounting for 727 incidents, all classified as "Assault Offenses." Property crime is also significant, with a total of 571 incidents, broken down into "Larceny/Theft Offenses" (277), "Stolen Property Offenses" (4), "Motor Vehicle Theft" (26), "Burglary/Breaking & Entering" (24), and "Destruction/Damage/Vandalism of Property" (240). Other notable categories include "Robbery," with 27 incidents, all categorized as "Robbery." "Sex Offenses" account for 21 incidents, including "Sex Offenses" (18), "Kidnapping/Abduction" (2), and "Sex Offenses, Non-forcible" (1). "Homicide" is the least frequent category, with 10 incidents, split between two entries of "Homicide Offenses" (7 and 3). The "Others" category encompasses 48 incidents, including "Human Trafficking" (1), "Embezzlement" (8), "Counterfeiting/Forgery" (5), "Fraud Offenses" (29), and "Arson" (5). To provide a more comprehensive safety assessment, additional data is needed. This includes the city and state names associated with GEOID 16000US4224000, the population of the area, and the median income. With this information, we can calculate the crime rate per 100,000 residents, assign a safety score and grade, and determine the violent and property crime rates. Furthermore, zip code-level data and crime map visualizations would enhance the analysis, offering a more granular understanding of crime distribution. Without this contextual data, the insights are limited to the relative frequency of different crime categories within the provided dataset.
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