How daily temperature — and rain, and snow — shape the volume and the type of crime reported across the city, and why some offenses ride the weather while others ignore it.
Period: 2017-01-01 → 2026-06-03Incidents: 791,583Days analyzed: 3,441Crime: Detroit Open Data PortalWeather: Open-Meteo reanalysis
01 — The headlineWarmer days are busier days for Detroit police
Across 3,441 days, the daily mean temperature tracks about 36% of the variance in daily crime counts (Pearson r = 0.60) — though most of that is the shared seasonal swing, not genuine day-to-day movement. Stripped of the calendar, the link is weaker but real: warmer-than-normal days still carry more crime than their season predicts (about 10% of the within-season variance, r = 0.32; section 04). The relationship is strong, positive, and broadly monotonic (though a quadratic term detects mild curvature, so the per-10°F figure is best read as an average slope across the observed range): each additional 10°F is associated with roughly 5% more reported crime.
Each dot is one day. Color encodes temperature, from cold blue to hot red. The trend line is a descriptive least-squares fit; the headline percentages come from the Poisson model.
Takeaway. The coldest days (<20°F) average just 181 reported incidents; the hottest (75°F+) average 255 — a 41% jump. Heat doesn't only change how much crime occurs, but as the next sections show, which kinds.
02 — The seasonal engineCrime and temperature rise and fall together
Aggregated by month, the two series trace nearly the same arc: a winter trough in January–February and a summer peak in July–August. July, the hottest month, is also the single highest-crime month of the year.
This co-movement is the heart of the story — but it also raises a fair objection: maybe summer brings more crime for reasons that merely coincide with heat (school being out, longer daylight, more travel). Section 04 tackles that head-on.
03 — Not all crime is equalHeat inflames confrontation, not paperwork
Breaking the effect out by offense category reveals a sharp split. Violent and interpersonal crimes are the most temperature-sensitive: aggravated assault, weapons offenses, and homicide all climb steeply with the mercury. Property and outdoor-opportunity crimes respond moderately. And administrative or indoor offenses — fraud, drug cases, court-process violations — barely move at all.
Slope of daily incidents on temperature, scaled to each category's own average. Several categories show detectable curvature, so for those the bar is the average slope across the observed temperature range rather than a constant rate at every temperature. Bar length is the raw per-10°F slope; solid bars survive deseasonalizing (the within-season test of section 04), while faded bars do not — "seasonal" marks an effect that is detectable overall but not within season, and "n.s." one that is not significant at all.
The heatmap below makes the texture vivid. Read across a row: deep red means a category runs well above its yearly average on hot days, deep blue means well below. Aggravated assault and weapons offenses swing from ~0.69× their average in deep cold to ~1.29× in heat — a near-doubling. Fraud and drug cases stay flat all the way across.
Takeaway. The "heat → aggression" pattern long documented in criminology shows up cleanly here: temperature acts most on impulsive, face-to-face violence, and least on premeditated or indoor offenses.
04 — Is it really the heat?Yes — even after stripping out the seasons
To separate temperature from everything else that makes summer summer, we computed anomalies: we fit each series a smooth seasonal cycle (a low-order harmonic curve through the year) and subtract it, leaving only how much warmer-or-cooler and higher-or-lower-crime each specific day was than its time of year would predict. If a 70°F day in May behaves like a 70°F day in September, season — not heat — would be doing the work, and the anomaly correlation would vanish.
It doesn't. Warmer-than-normal days still carry more crime than normal (r = 0.32), and the effect is strongest for the high-volume violent categories flagged above (aggravated assault, assault, weapons, homicide). A few lower-volume offenses whose raw temperature slope looked steep — sexual assault, disorderly conduct, OUIL — lose significance once the season is stripped out, a reminder that the per-category “sensitivity” in section 03 still carries seasonal confounding. But for the violent core, an unseasonably hot day is a higher-crime day on its own.
Takeaway. The temperature signal survives deseasonalizing. This is a genuine same-day association between heat and crime, not just a calendar artifact.
05 — The clockHeat hits hardest after dark
Joining each violent incident to the ambient air temperature in that very hour, the dose-response is strikingly clean: the city averages 2.6 violent incidents in a sub-20°F hour, rising to 4.6 in a 75°F+ hour — a 76% increase, hour for hour.
But the heat doesn't act evenly around the clock. Comparing the warmest third of days with the coldest third, daytime violence (8 am–6 pm) runs about 21% higher on hot days — while the late-night window (8 pm–3 am) jumps 43% higher. Warm evenings keep people outdoors and in contact long after a cold night would have emptied the streets.
Average violent incidents per day in each hour, warmest-third vs coldest-third days. The red–blue gap is widest in the evening.
Takeaway. Temperature's effect on violence is concentrated in the evening and overnight hours — the "long warm night" is the real risk window.
06 — The calendarHot weekends are the most violent days of all
Weekends already carry more violence than weekdays at every temperature, and heat lifts both. Stacking the two effects, a hot summer weekend averages 116 violent incidents a day — versus just 65 on a frigid weekday, a 79% swing from the calm extreme to the volatile one. Across the temperature range, weekends run about 10 extra violent incidents per day above weekdays.
For total crime, the temperature slope is nearly identical on weekends (+5.0% per 10°F) and weekdays (+4.7%) — heat raises the baseline rather than steepening it. The weekend's distinct signature is in violence specifically, where social activity and temperature compound.
Takeaway. Temperature and the weekend are roughly additive for violent crime; the hottest weekends sit at the top of the risk distribution.
07 — The mapHeat sensitivity is a downtown, riverfront story
Crime is not spread evenly across Detroit, and neither is its responsiveness to heat. The density map below traces the familiar geography — corridors along the major avenues, concentration through the greater downtown core, the river defining the southern edge.
Coloring each neighborhood by its temperature sensitivity reveals a pattern with a clear lead: entertainment and riverfront districts dominate the top of the list. Among the 88 of 91 neighborhoods whose slope is statistically distinguishable from zero (Benjamini-Hochberg FDR < 0.05), the three most sensitive are Rivertown (+14% per 10°F), Greektown (+13%), and Grixdale Farms (+10%) — the first two riverfront/nightlife districts where warm weather draws crowds to bars, festivals, and the riverwalk, the third a reminder that the effect is not exclusively a downtown one. Downtown proper itself runs hot (+8%), while quieter residential neighborhoods hover near the citywide +4–5%. The ranking is of noisy per-neighborhood point estimates, so read the broad pattern, not the exact order.
Each bubble is a neighborhood (≥3,000 incidents): size = total volume, color = % more crime per +10°F.
At the precinct level the effect is broad but graded — every one of Detroit's precincts shows a positive point estimate (from +3.8% to +6.4% per 10°F), all individually significant after FDR correction.
Precinct
Incidents
/day
% violent
Per +10°F
r
Precinct 08
100,525
29.2
40%
+3.8%
0.29
Precinct 09
96,392
28.0
44%
+5.1%
0.36
Precinct 03
84,017
24.4
31%
+4.8%
0.25
Precinct 12
83,294
24.2
37%
+4.5%
0.31
Precinct 06
79,006
23.0
43%
+4.4%
0.30
Precinct 02
71,238
20.7
43%
+5.3%
0.34
Precinct 10
62,809
18.3
41%
+4.8%
0.30
Precinct 11
61,531
17.9
40%
+4.7%
0.28
Precinct 05
54,853
15.9
38%
+5.0%
0.29
Precinct 07
51,634
15.0
32%
+6.4%
0.33
Precinct 04
44,575
13.0
40%
+4.8%
0.24
Takeaway. The temperature–crime link holds citywide, but it is sharpest where heat changes how people use public space — the downtown core and the riverfront entertainment districts.
08 — The skyRain quiets violence; snow blunts theft
Temperature is not the only thing the weather does. Across 1,668 wet days and 1,773 dry ones, precipitation leaves its own mark — but because rain and snow are tangled up with temperature (it only snows when it's cold), every figure here holds temperature constant via regression, isolating the precipitation effect itself.
The clearest signal is on violent crime: a wet day sees about -5% fewer violent incidents than a dry day at the same temperature. Property crime barely moves (-1.4%), and administrative offenses — fraud, warrants, court process — are statistically untouched (-0.0%, n.s.). Rain keeps would-be antagonists indoors and off the streets; it does little to stop a fraud report from being filed.
And the suppression is not a fluke of warm rainy days — it holds inside every temperature band, from freezing to sweltering, each wet column sitting a few percent below its dry neighbor.
Rain and snow do different jobs. Separating precipitation by type — again at equal temperature — uncovers a clean split. The effects are marginal rates per inch (most days see only a fraction of an inch, so these scale a full inch out to the wetter tail): an inch of rain is associated with about 10 fewer violent incidents on the day but leaves theft essentially alone. An inch of snow does the opposite: it cuts property crime by roughly 6 incidents — buried cars, shuttered storefronts and empty sidewalks shrink the opportunity for larceny and break-ins — while denting violence only modestly.
Regression coefficients per inch of precipitation, controlling for daily mean temperature. "n.s." = not significant. Note the two scales differ physically: rain is liquid depth (a typical wet day ≈ 0.09 in) while snow is snow depth (≈ 7× the water equivalent; a typical snow day ≈ 0.5 in), so a full inch extrapolates farther into the tail for rain than for snow — read each bar against its own typical event, not against the other.
For reference, here is the raw picture by sky condition. Snow days look dramatically calmer — but most of that gap is the cold they ride in on, which is why the temperature-controlled figures above tell the more honest story.
Sky condition
Days
Avg temp
All crime/day
Violent/day
Property/day
Clear
144
58°
242
97
110
Cloudy
1,330
48°
231
91
106
Drizzle
823
59°
239
95
110
Rain
627
61°
237
92
111
Snow
517
29°
202
76
94
Takeaway. Wet weather suppresses crime independently of temperature — rain chiefly calms violence, snow chiefly blunts property crime, and neither touches paperwork offenses.
09 — The wind & the stormRough weather keeps the peace
Wind tells the same story as rain, and tells it independently. Holding both temperature and precipitation fixed, each extra 10 mph of peak wind shaves about 2.5 violent incidents off the day (p < 0.001) — yet leaves property crime essentially untouched (+0.2/day, n.s.). A blustery day is an uncomfortable day to be loitering on a corner.
Bundling the rough days together — the 480 "storm days" with heavy rain (>0.5 in) or strong wind (top-decile, ≥20 mph) — violent crime runs about 7% below a calm day of the same temperature.
Step back and a single mechanism organizes the whole report. Every weather condition that makes the outdoors less hospitable — rain, wind, storms — pushes violence down by a similar ~5–7%. Only heat, which makes the outdoors more inviting, pushes it up. Violence in Detroit is, in large part, a function of how many people are outside and in contact with one another.
Each bar is one condition's effect on violent crime versus a day of equal temperature — a simplified single-factor view (the wind and rain sections above add finer joint controls). The bars are not mutually exclusive: "storm" is defined as heavy rain or top-decile wind, so it overlaps the windy-day bar rather than measuring a separate set of days. Heat is the lone condition that increases it.
Takeaway. The thread tying heat, rain, wind and storms together is street exposure: pleasant weather populates public space and friction follows; harsh weather empties it and violence recedes.
10 — The long hot spellA heat wave is no worse than its hottest day
If heat fuels aggression, do tempers compound over a prolonged hot spell? We flagged Detroit's heat waves — runs of 3+ consecutive days topping 85°F (the 35 such streaks cover 80 days) — and asked whether being deep into one adds crime beyond what the day's own temperature predicts.
It does not. Comparing day 3+ of a heat wave to an isolated hot day of the same temperature, the difference is a statistically insignificant +1.1 violent incidents (p = 0.64) — and likewise flat for total crime. Crime rises with the thermometer each day and resets with it; the heat does not "bank."
Takeaway. Heat's effect is contemporaneous, not cumulative. For forecasting risk, today's temperature matters; how long the hot streak has run does not.
11 — Every category, rankedOne row per offense: heat, rain and snow
The complete picture for all 23 offense categories with enough volume to model. Per +10°F is the temperature sensitivity; Rain and Snow give the change per inch of each (as % of the category's average), both holding temperature constant. Green = more crime, red = less; a small dot (·) flags results that are not statistically significant.
Offense category
Type
Total
/day
Per +10°F
r
Rain /in
Snow /in
Heat corr.
Disorderly Conduct
Violent
2,627
0.8
+10.0%
0.13
-31%
-23%
Homicide
Violent
2,516
0.7
+9.9%
0.14
-1%·
-4%·
Aggravated Assault
Violent
78,657
22.9
+9.8%
0.54
-12%
-4%
Ouil
Other
4,876
1.4
+9.7%
0.13
+2%·
-23%
Weapons Offenses
Violent
29,417
8.6
+9.6%
0.27
-30%
-10%
Sex Offenses
Violent
9,962
2.9
+9.5%
0.12
-28%
-16%
Other
Other
2,075
0.6
+8.4%
0.10
-7%·
-0%·
Arson
Violent
5,770
1.7
+7.6%
0.16
-12%·
-8%·
Runaway
Other
5,903
1.7
+6.3%
0.11
-14%·
-5%·
Damage To Property
Property
103,250
30.0
+6.2%
0.42
-3%·
-6%
Robbery
Violent
16,286
4.7
+5.2%
0.16
-1%·
-5%·
Obstructing The Police
Violent
10,840
3.1
+5.2%
0.12
-20%
-14%
Miscellaneous
Other
2,414
0.7
+4.6% n.s.
0.04
+22%·
-16%·
Assault
Violent
148,063
43.0
+4.6%
0.35
-7%
-4%
Stolen Property
Property
17,681
5.1
+4.6%
0.11
-24%
-9%·
Family Offense
Other
5,035
1.5
+4.2% n.s.
0.06
+2%·
-9%·
Stolen Vehicle
Property
69,932
20.3
+4.1%
0.22
+0%·
-6%
Larceny
Property
117,160
34.0
+3.8%
0.26
-3%·
-6%
Sexual Assault
Violent
6,475
1.9
+3.8%
0.08
+9%·
-4%·
Burglary
Property
56,810
16.5
+1.8%
0.09
+11%
-4%·
Obstructing Judiciary
Other
10,200
3.0
+1.2% n.s.
0.03
-6%·
+1%·
Dangerous Drugs
Other
13,254
3.9
+0.9% n.s.
0.02
-2%·
-11%
Fraud
Other
65,853
19.1
+0.5% n.s.
0.03
+2%·
-0%·
Reading the precipitation columns: most street-facing offenses turn red in the wet (weapons -30%/in rain, disorderly conduct -31%), while burglary stands out in green — break-ins actually rise with rain (+11%/in), the cover of bad weather apparently working in the burglar's favor.
Methodology & caveats
Crime data. Detroit Police Department incident records via the Detroit Open Data Portal (RMS export). Analysis restricted to 2017-01-01–2026-06-03, the window with consistent reporting (2017 onward); sparse legacy records back to 1915 were excluded. Each incident's UTC timestamp was converted to America/Detroit local time before assigning it to a calendar day.
Weather data. Daily mean/max/min 2 m air temperature for downtown Detroit (42.33°N, 83.05°W) from the Open-Meteo historical reanalysis archive, in °F. Daily mean temperature is used throughout.
Method. Incidents were aggregated to daily counts overall and per offense category, then joined to weather by date. Each weather effect is fit with a Poisson pseudo-maximum-likelihood model (log link): the coefficients are consistent for the conditional mean even though daily crime is over-dispersed, and a +10°F change is reported as the multiplicative effect (e10β−1)×100% (absolute per-day effects are evaluated at the series mean). Pearson and Spearman correlations are also reported as descriptive measures, alongside a within-season check that asks whether the temperature signal survives stripping out the calendar. That check is the same Poisson count model with the smooth seasonal cycle entered directly as harmonic (Fourier) nuisance controls — a low-order day-of-year fit, leap-year safe — so temperature is identified only from how much warmer-or-cooler each day was than its time of year. Folding the seasonal terms into the single count model (rather than deseasonalizing the two series in a separate first step and regressing one residual on the other) follows the Frisch–Waugh–Lovell intuition — exact for OLS, and for this log-link Poisson model close but not numerically identical — while keeping the Newey-West standard errors correctly calibrated, instead of treating an estimated seasonal residual as a known regressor. The deseasonalized correlation r quoted alongside (and the anomaly scatter, fig 6) is the descriptive two-step measure, not a test. Standard errors are robust by construction: because counts are both over-dispersed and serially correlated (residual lag-1 autocorrelation ≈ 0.3), every p-value uses a Newey-West (HAC) sandwich on the Poisson score — a Bartlett kernel with the automatic rule-of-thumb bandwidth (lag = 4·(n/100)2/9) — which is valid under both, where i.i.d. errors would overstate significance by roughly 2–3× (the standard error for the total-crime series roughly triples from nonrobust to HAC). Every count model on the full daily series also includes day-of-week indicators as nuisance controls, soaking up Detroit's strong weekly cycle so it cannot leak into the weather coefficients; because day-of-week is essentially uncorrelated with temperature the point estimates barely move, but the inference is cleaner (the subsample tests on non-contiguous slices — weekday/weekend and hot-days-only — omit them, as full-week indicators do not apply there). The log-linear temperature term is checked for curvature by adding a centered quadratic term per category: for total crime it is significant (p = 2.9e-07): with ~3,400 days even slight curvature is detectable, so the per-10°F figure should be read as the average slope across the observed temperature range rather than a constant rate at every temperature. The same quadratic check flags detectable curvature in several individual offense categories too, so their per-10°F figures (the section-03 bars and the per-category table) are likewise average slopes across the observed range rather than a constant rate at every temperature. Across the per-offense tables, significance flags are Benjamini-Hochberg FDR-corrected within each family of category-level tests (temperature, wet, rain, snow), and the per-neighborhood and per-precinct temperature slopes are FDR-corrected within their own families too, so an isolated "significant" unit among two dozen is not mistaken for a real effect; the headline all-crime row and the four family aggregates are primary hypotheses and keep their raw p-values. The section-level claims that are not part of those category families — the wind, storm, and heat-wave tests, and the family-aggregate wet/rain/snow effects — are treated as pre-specified primary hypotheses and are not multiplicity-adjusted across sections; readers weighing the full set of ~20 such tests should bear that wider family in mind. Regression-line overlays in the scatter figures (figs 1 and 6) are descriptive ordinary-least-squares fits for visual orientation; the quoted percentage effects always come from the Poisson model, so a fitted line's slope and the body's per-10°F figure need not coincide.
Time of day. The hourly analysis joins each incident to Open-Meteo's hourly 2 m temperature for the matching local clock-hour. "Hot" and "cold" days are the warmest and coldest thirds of days by daily mean temperature. "Violent" is a broad violent/interpersonal grouping: assault, aggravated assault, weapons, robbery, homicide, sexual/sex offenses, arson, disorderly conduct, and obstructing police — note this reaches beyond the strict UCR violent definition to include arson and the confrontational public-order offenses (disorderly conduct, obstructing police), which together are only ~4% of the group and do not drive its results. "Property" groups larceny, damage, stolen vehicle, burglary, and stolen property. Records whose timestamp falls exactly on midnight (00:00:00) or noon (12:00:00) are unknown-time placeholders — together ~5% of incidents, far above chance — and are excluded from all hour-of-day analyses (but retained in daily counts, where the calendar day is still valid).
Geography. Incidents are geocoded to the nearest street intersection (visible as faint gridding in the density map). Neighborhood sensitivity is computed only for neighborhoods with ≥3,000 incidents; precinct figures cover all 11 of Detroit's numbered precincts (a small number of incidents carry placeholder or invalid precinct codes and are excluded). Each neighborhood and precinct temperature slope carries its own HAC p-value, FDR-corrected within its set; units that do not clear FDR < 0.05 are flagged n.s. and the "most heat-sensitive" ranking is drawn only from those that do, since ranking noisy point estimates otherwise favours whichever unit's sampling error pointed up. Bubble positions are median incident coordinates, not official boundaries.
Precipitation. Daily precipitation, rain, and snowfall totals (inches) and WMO weather codes come from the same Open-Meteo archive. Because precipitation co-varies with temperature, all precipitation effects come from the same Poisson model with daily mean temperature included as a control (and, for the rain-vs-snow split, rain and snowfall amounts entered separately); the by-condition table is raw and is labelled as temperature-confounded. A "wet day" is ≥0.01 in of precipitation.
Wind & storms. Daily maximum 10 m wind speed (mph) from the same archive. Wind effects come from regressions controlling for both temperature and precipitation. "Storm days" are the upper-decile wind days (≥20 mph) or heavy-rain days (>0.5 in); the ERA5 archive does not flag thunderstorms separately, so no lightning/thunder classification is used.
Heat waves. Defined as 3+ consecutive days with a daily high ≥85°F (using daily maximum, not mean, temperature). The cumulative test regresses daily crime (Poisson, log link) on the day's maximum temperature plus an indicator for being on day 3 or later of a wave, so any heat-wave coefficient is the effect beyond that day's heat. Because this test runs on the hot-days-only sub-sample — which is not a contiguous daily series — it uses heteroskedasticity-robust White standard errors (the HC3 variant, whose leverage correction keeps the small ~250-row sub-sample well-calibrated) rather than the HAC lags used elsewhere.
Correlation, not causation. These are observational associations. Heat plausibly increases crime through more time outdoors, more social contact, and the well-studied link between temperature and aggression — but daylight hours, school calendars, and seasonal routines move with temperature and are not separately controlled here (the anomaly analysis mitigates, not eliminates, this). The deseasonalizing removes only the smooth within-year cycle, not any multi-year trend or one-off shock (e.g. 2020); because temperature anomalies are essentially uncorrelated with the calendar year, such residual structure adds noise to the crime anomaly rather than correlated signal, so it biases the deseasonalized correlation toward zero — the reported anomaly effect is, if anything, conservative.
Reporting effects. Figures count reported incidents. Categories such as fraud reflect when a report was filed rather than when conduct occurred, which dampens any weather signal. Recent weeks may be revised upward as cases are entered, so not-yet-complete trailing days (any final day whose count falls below half a reference median — the median of a 90-day window ending 30 days before the last day, kept that far back so the reference itself stays outside the under-reported tail) are dropped from the analysis window rather than entering the regressions as artificial near-zero days.