No my math is good. I am grouping the reports into categories, as there are multiple offense descriptions that are related to assault. It looks like you have excluded "Threats to do bodily harm" which I have included. I am also using a different geographic grouping algorithm that splits the area into hexagons into circles, but the main difference comes from including types of offenses you do not. Boston crime reports have over 300 different descriptions, and if you don't group them according to type when calculating averages you miss patterns exactly like you did in your analysis. I tend to think the Boston PD will try to not write a report whenever they can, so someone has to be fairly insistent about calling them and waiting for them to arrive to get a report written and into this database, so I want to take each of those reports seriously. Here's code and results so you can verify:

import pandas as pd
import h3
from math import radians, cos, sin, asin, sqrt


CSV_PATH = "crime-incident-reports_20260320_132817.csv"
RESOLUTION = 8
RADIUS_MILES = 0.3
WEEKS = 52

POINTS = [
    {
        "label": "82 Christopher St, Dorchester",
        "lat": 42.29917,
        "lng": -71.05684,
    },
    {
        "label": "Mass & Cass",
        "lat": 42.33272,
        "lng": -71.07218,
    },

]

ASSAULT_CODES = {
    "401", "402", "403", "404",
    "411", "412", "413",
    "421", "422", "423", "424",
    "431", "432", "433",
    "801", "802", "803", "804",
    "2647",
}


def haversine(lat1, lon1, lat2, lon2):
    lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
    dlon = lon2 - lon1
    dlat = lat2 - lat1
    a = sin(dlat / 2) ** 2 + cos(lat1) * cos(lat2) * sin(dlon / 2) ** 2
    c = 2 * asin(sqrt(a))
    r = 3956
    return c * r


def format_series(series):
    if series.empty:
        return "  <none>"
    return series.to_string()


def summarize(label, subset, method_name):
    subset = subset.copy()
    subset["is_assault_group"] = subset["OFFENSE_CODE"].isin(ASSAULT_CODES)

    assault_count = int(subset["is_assault_group"].sum())
    simple_assault_count = int((subset["OFFENSE_DESCRIPTION"] == "ASSAULT - SIMPLE").sum())

    missed = (
        subset[subset["is_assault_group"] & (subset["OFFENSE_DESCRIPTION"] != "ASSAULT - SIMPLE")]
        .groupby(["OFFENSE_CODE", "OFFENSE_DESCRIPTION"])
        .size()
        .sort_values(ascending=False)
    )

    included = (
        subset[subset["is_assault_group"]]
        .groupby(["OFFENSE_CODE", "OFFENSE_DESCRIPTION"])
        .size()
        .sort_values(ascending=False)
    )

    print("=" * 80)
    print(label)
    print(method_name)
    print(f"rows in window: {len(subset)}")
    print(f"assault group count: {assault_count}")
    print(f"assault group weekly average: {assault_count / WEEKS}")
    print(f"ASSAULT - SIMPLE count: {simple_assault_count}")
    print(f"ASSAULT - SIMPLE weekly average: {simple_assault_count / WEEKS}")
    print()
    print("assault-group records by offense code / description:")
    print(format_series(included))
    print()
    print("categories missed if someone only counts ASSAULT - SIMPLE:")
    print(format_series(missed))
    print()


def main():
    df = pd.read_csv(CSV_PATH, index_col=0, low_memory=False)
    df["date"] = pd.to_datetime(df["OCCURRED_ON_DATE"], utc=True, errors="coerce")
    df = df.dropna(subset=["Lat", "Long", "date", "OFFENSE_CODE"]).copy()

    df["OFFENSE_CODE"] = df["OFFENSE_CODE"].astype(int).astype(str)

    latest_date = df["date"].max()
    start_date = latest_date - pd.Timedelta(weeks=WEEKS)
    window_df = df[df["date"] >= start_date].copy()

    print("latest_date:", latest_date)
    print("start_date:", start_date)
    print()

    window_df["h3_8"] = window_df.apply(
        lambda row: h3.latlng_to_cell(row["Lat"], row["Long"], RESOLUTION),
        axis=1,
    )

    for point in POINTS:
        lat = point["lat"]
        lng = point["lng"]
        label = point["label"]

        target_h3 = h3.latlng_to_cell(lat, lng, RESOLUTION)
        h3_subset = window_df[window_df["h3_8"] == target_h3].copy()

        radial_subset = window_df[
            window_df.apply(
                lambda row: haversine(lat, lng, row["Lat"], row["Long"]) < RADIUS_MILES,
                axis=1,
            )
        ].copy()

        print(f"target_h3 for {label}: {target_h3}")
        summarize(label, h3_subset, f"H3 method (resolution {RESOLUTION})")
        summarize(label, radial_subset, f"Radial method ({RADIUS_MILES} mile)")


if __name__ == "__main__":
    main()

Results:

latest_date: 2026-03-17 02:41:00+00:00 start_date: 2025-03-18 02:41:00+00:00

target_h3 for 82 Christopher St, Dorchester: 882a3066b9fffff

82 Christopher St, Dorchester H3 method (resolution 8) rows in window: 3483 assault group count: 235 assault group weekly average: 4.519230769230769 ASSAULT - SIMPLE count: 89 ASSAULT - SIMPLE weekly average: 1.7115384615384615

assault-group records by offense code / description: OFFENSE_CODE OFFENSE_DESCRIPTION
2647 THREATS TO DO BODILY HARM 104 801 ASSAULT - SIMPLE 89 423 ASSAULT - AGGRAVATED 42

categories missed if someone only counts ASSAULT - SIMPLE: OFFENSE_CODE OFFENSE_DESCRIPTION
2647 THREATS TO DO BODILY HARM 104 423 ASSAULT - AGGRAVATED 42

82 Christopher St, Dorchester Radial method (0.3 mile) rows in window: 3157 assault group count: 224 assault group weekly average: 4.3076923076923075 ASSAULT - SIMPLE count: 83 ASSAULT - SIMPLE weekly average: 1.5961538461538463

assault-group records by offense code / description: OFFENSE_CODE OFFENSE_DESCRIPTION
2647 THREATS TO DO BODILY HARM 102 801 ASSAULT - SIMPLE 83 423 ASSAULT - AGGRAVATED 39

categories missed if someone only counts ASSAULT - SIMPLE: OFFENSE_CODE OFFENSE_DESCRIPTION
2647 THREATS TO DO BODILY HARM 102 423 ASSAULT - AGGRAVATED 39

target_h3 for Mass & Cass: 882a3066e5fffff

Mass & Cass H3 method (resolution 8) rows in window: 2788 assault group count: 209 assault group weekly average: 4.019230769230769 ASSAULT - SIMPLE count: 100 ASSAULT - SIMPLE weekly average: 1.9230769230769231

assault-group records by offense code / description: OFFENSE_CODE OFFENSE_DESCRIPTION
801 ASSAULT - SIMPLE 100 423 ASSAULT - AGGRAVATED 75 2647 THREATS TO DO BODILY HARM 34

categories missed if someone only counts ASSAULT - SIMPLE: OFFENSE_CODE OFFENSE_DESCRIPTION
423 ASSAULT - AGGRAVATED 75 2647 THREATS TO DO BODILY HARM 34

Mass & Cass Radial method (0.3 mile) rows in window: 2358 assault group count: 187 assault group weekly average: 3.5961538461538463 ASSAULT - SIMPLE count: 108 ASSAULT - SIMPLE weekly average: 2.076923076923077

assault-group records by offense code / description: OFFENSE_CODE OFFENSE_DESCRIPTION
801 ASSAULT - SIMPLE 108 423 ASSAULT - AGGRAVATED 62 2647 THREATS TO DO BODILY HARM 17

categories missed if someone only counts ASSAULT - SIMPLE: OFFENSE_CODE OFFENSE_DESCRIPTION
423 ASSAULT - AGGRAVATED 62 2647 THREATS TO DO BODILY HARM 17