Initial evidence on the relationship between the coronavirus pandemic and crime in the United States

Ashby, Matthew P. J.

doi:10.1186/s40163-020-00117-6

Crime Science

Table 1 Terms used in SARIMA models for estimating crime frequency without coronavirus. All models had zero seasonal MA periods

From: Initial evidence on the relationship between the coronavirus pandemic and crime in the United States

Crime type	City	Min	Mean	SD	Max	AR periods	MA periods	Seasonal AR periods	Degrees of freedom	MASE
Serious assaults in public	Austin, TX	36	65	11	89	4	0	1	152	0.57
	Baltimore, MD	20	65	18	113	1	1	1	154	0.55
	Dallas, TX	16	36	10	64	0	0	1	106	0.55
	Los Angeles, CA	130	210	31	281	3	0	1	153	0.58
	Louisville, KY	1	9	3	18	2	0	1	154	0.61
	Montgomery County, MD	0	5	2	12	0	1	1	130	0.56
	Nashville, TN	5	62	33	169	3	0	1	154	0.34
	Phoenix, AZ	23	42	8	63	0	0	1	156	0.54
Serious assaults in residences	Austin, TX	58	89	12	136	2	1	1	153	0.56
	Baltimore, MD	3	35	8	60	2	0	1	154	0.58
	Dallas, TX	6	19	7	45	0	1	1	105	0.47
	Los Angeles, CA	68	106	16	162	1	1	1	154	0.57
	Louisville, KY	2	11	6	36	1	1	1	154	0.46
	Montgomery County, MD	1	7	3	17	0	0	1	131	0.55
	Nashville, TN	6	51	23	122	1	2	1	153	0.31
	Phoenix, AZ	25	49	10	79	1	1	1	154	0.44
Residential burglary	Austin, TX	20	53	13	101	5	0	1	151	0.44
	Baltimore, MD	15	80	23	139	2	0	1	154	0.41
	Boston, MA	10	28	8	52	2	0	1	154	0.48
	Chicago, IL	202	376	82	584	4	1	1	151	0.35
	Los Angeles, CA	145	209	28	293	4	0	1	152	0.46
	Louisville, KY	38	70	16	111	3	0	1	153	0.49
	Memphis, TN	59	123	22	198	1	1	1	154	0.52
	Minneapolis, MN	15	51	16	107	2	0	1	154	0.46
	Montgomery County, MD	4	21	6	40	3	0	1	128	0.46
	Phoenix, AZ	86	143	28	219	1	0	1	155	0.39
	San Francisco, CA	28	53	10	81	1	1	1	154	0.45
Non-residential burglary	Austin, TX	11	33	9	59	1	1	1	154	0.56
	Baltimore, MD	20	49	18	162	2	1	1	153	0.44
	Chicago, IL	42	89	21	174	2	0	1	154	0.51
	Los Angeles, CA	69	119	20	184	1	1	1	154	0.52
	Louisville, KY	11	31	8	57	1	1	1	154	0.56
	Memphis, TN	13	42	12	87	1	1	1	154	0.47
	Minneapolis, MN	2	13	5	33	1	1	1	154	0.56
	Montgomery County, MD	0	7	5	52	0	0	1	131	0.57
	Philadelphia, PA	13	26	8	70	5	0	1	151	0.58
	Phoenix, AZ	24	53	9	80	2	0	1	154	0.47
	San Francisco, CA	14	51	25	111	1	1	1	155	0.23
Theft of vehicle	Austin, TX	23	45	11	78	1	1	1	154	0.49
	Baltimore, MD	46	82	15	145	2	0	1	154	0.50
	Chicago, IL	264	398	65	596	2	0	1	154	0.50
	Los Angeles, CA	255	344	37	462	3	0	1	153	0.41
	Louisville, KY	47	78	13	118	4	0	1	152	0.45
	Memphis, TN	38	79	17	163	3	1	1	152	0.41
	Minneapolis, MN	15	44	14	131	1	1	0	155	0.51
	Montgomery County, MD	7	17	5	41	0	0	1	131	0.58
	Philadelphia, PA	23	44	9	69	1	0	1	155	0.54
	Phoenix, AZ	89	132	16	181	0	0	1	156	0.54
	San Francisco, CA	68	105	19	157	1	1	1	154	0.45
	Tucson, AZ	23	43	10	74	1	1	1	154	0.46
	Washington, DC	25	46	11	90	1	2	1	153	0.50
Theft from vehicle	Austin, TX	120	202	39	325	1	2	1	153	0.39
	Baltimore, MD	54	118	27	217	0	1	1	155	0.46
	Los Angeles, CA	537	632	41	743	2	0	1	154	0.49
	Louisville, KY	50	97	20	156	3	1	1	152	0.45
	Memphis, TN	82	149	31	228	1	1	1	154	0.52
	Minneapolis, MN	24	70	21	128	3	0	1	153	0.46
	Montgomery County, MD	25	85	18	141	1	1	1	129	0.42
	Philadelphia, PA	149	258	39	369	1	1	1	154	0.52
	San Francisco, CA	337	545	84	743	1	1	1	154	0.33
	Tucson, AZ	40	73	15	116	2	1	1	153	0.38
	Washington, DC	108	214	41	320	2	0	1	154	0.38
Personal robbery	Baltimore, MD	47	90	20	164	0	2	1	154	0.51
	Boston, MA	6	18	6	42	2	3	1	151	0.55
	Chicago, IL	170	344	81	544	2	0	1	154	0.41
	Dallas, TX	0	62	18	108	0	0	1	110	0.57
	Los Angeles, CA	119	168	19	227	0	0	1	156	0.53
	Louisville, KY	5	17	6	36	3	0	1	153	0.53
	Memphis, TN	24	53	13	86	0	0	1	156	0.58
	Minneapolis, MN	7	26	9	51	2	2	1	152	0.44
	Montgomery County, MD	0	7	4	21	0	1	1	130	0.55
	Philadelphia, PA	49	98	18	159	0	0	1	156	0.54
	San Francisco, CA	8	20	5	34	0	1	1	155	0.58
	Tucson, AZ	3	12	4	24	1	2	1	153	0.56

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