Data
Violent-Crime-by-Counties-in-Maryland

Violent-Crime-by-Counties-in-Maryland

active ARFF GPL 2 Visibility: public Uploaded 23-03-2022 by Elif Ceren Gok
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  • Computer Systems Machine Learning
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Context Crime rates vary across space and time. The reasons crimes are committed in some places but not others can be difficult to detect because of complex socio-economic factors, but policymakers still need to understand how crime rates are changing from place to place and from time to time to inform their policies. Many government statistics, such as crime rates, come from nested datasets. Most US States are divided into counties (Alaska has burrows, and Louisiana has parishes), and counties and county-level governments can vary within the same state. For example, one county might have a high population density and be urban, whereas a second county might have a low population density and be rural.

38 features

JURISDICTIONstring26 unique values
0 missing
YEARstring42 unique values
0 missing
POPULATIONnumeric1001 unique values
0 missing
MURDERnumeric112 unique values
0 missing
RAPEnumeric228 unique values
0 missing
ROBBERYnumeric397 unique values
0 missing
AGG._ASSAULTnumeric603 unique values
0 missing
B_&_Enumeric746 unique values
0 missing
LARCENY_THEFTnumeric907 unique values
0 missing
M/V_THEFTnumeric509 unique values
0 missing
GRAND_TOTALnumeric950 unique values
0 missing
PERCENT_CHANGEnumeric355 unique values
24 missing
VIOLENT_CRIME_TOTALnumeric673 unique values
0 missing
VIOLENT_CRIME_PERCENTnumeric188 unique values
0 missing
VIOLENT_CRIME_PERCENT_CHANGEnumeric511 unique values
24 missing
PROPERTY_CRIME_TOTALSnumeric930 unique values
0 missing
PROPERTY_CRIME_PERCENTnumeric188 unique values
0 missing
PROPERTY_CRIME_PERCENT_CHANGEnumeric363 unique values
24 missing
OVERALL_CRIME_RATE_PER_100,000_PEOPLEnumeric1000 unique values
0 missing
OVERALL_PERCENT_CHANGE_PER_100,000_PEOPLEnumeric357 unique values
24 missing
VIOLENT_CRIME_RATE_PER_100,000_PEOPLEnumeric938 unique values
0 missing
VIOLENT_CRIME_RATE_PERCENT_CHANGE_PER_100,000_PEOPLEnumeric515 unique values
24 missing
PROPERTY_CRIME_RATE_PER_100,000_PEOPLEnumeric991 unique values
0 missing
PROPERTY_CRIME_RATE_PERCENT_CHANGE_PER_100,000_PEOPLEnumeric355 unique values
24 missing
MURDER_PER_100,000_PEOPLEnumeric190 unique values
0 missing
RAPE_PER_100,000_PEOPLEnumeric424 unique values
0 missing
ROBBERY_PER_100,000_PEOPLEnumeric747 unique values
0 missing
AGG._ASSAULT_PER_100,000_PEOPLEnumeric930 unique values
0 missing
B_&_E_PER_100,000_PEOPLEnumeric966 unique values
0 missing
LARCENY_THEFT_PER_100,000_PEOPLEnumeric991 unique values
0 missing
M/V_THEFT_PER_100,000_PEOPLEnumeric869 unique values
0 missing
MURDER__RATE_PERCENT_CHANGE_PER_100,000_PEOPLEnumeric626 unique values
24 missing
RAPE_RATE_PERCENT_CHANGE_PER_100,000_PEOPLEnumeric691 unique values
24 missing
ROBBERY_RATE_PERCENT_CHANGE_PER_100,000_PEOPLEnumeric652 unique values
24 missing
AGG._ASSAULT__RATE_PERCENT_CHANGE_PER_100,000_PEOPLEnumeric552 unique values
24 missing
B_&_E_RATE_PERCENT_CHANGE_PER_100,000_PEOPLEnumeric461 unique values
24 missing
LARCENY_THEFT__RATE_PERCENT_CHANGE_PER_100,000_PEOPLEnumeric370 unique values
24 missing
M/V_THEFT__RATE_PERCENT_CHANGE_PER_100,000_PEOPLEnumeric586 unique values
24 missing

19 properties

1008
Number of instances (rows) of the dataset.
38
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
312
Number of missing values in the dataset.
24
Number of instances with at least one value missing.
36
Number of numeric attributes.
0
Number of nominal attributes.
0.04
Number of attributes divided by the number of instances.
94.74
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
2.38
Percentage of instances having missing values.
Average class difference between consecutive instances.
0.81
Percentage of missing values.

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