Data
particulate-matter-ukair-2017

particulate-matter-ukair-2017

active ARFF Open Government Licence (OGL) Visibility: public Uploaded 04-12-2019 by Florian Pargent
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Hourly particulate matter air polution data of Great Britain for the year 2017, provided by Ricardo Energy and Environment on behalf of the UK Department for Environment, Food and Rural Affairs (DEFRA) and the Devolved Administrations on [https://uk-air.defra.gov.uk/]. The data was scraped from the UK AIR homepage via the R-package 'rdefra' [Vitolo, C., Russell, A., & Tucker, A. (2016, August). Rdefra: interact with the UK AIR pollution database from DEFRA. The Journal of Open Source Software, 1(4). doi:10.21105/joss.00051] on 09.11.2018. The data was published by DEFRA under the Open Government Licence (OGL) [http://www.nationalarchives.gov.uk/doc/open-government-licence/version/2/]. For a description of all variables, checkout the UK AIR homepage. The variable 'PM.sub.10..sub..particulate.matter..Hourly.measured.' was chosen as the target. The dataset also contains another measure of particulate matter 'PM.sub.2.5..sub..particulate.matter..Hourly.measured.' (ignored by default) which could be used as the target instead. The string variable 'datetime' (ignored by default) could be used to construct additional date/time features. In this version of the dataset, the features 'Longitude' and 'Latitude' were removed to increase the importance of the categorical features 'Zone' and 'Site.Name'.

10 features

PM.sub.10..sub..particulate.matter..Hourly.measured. (target)numeric21599 unique values
0 missing
datetimestring8760 unique values
0 missing
Hournumeric24 unique values
0 missing
Monthnominal12 unique values
0 missing
DayofWeeknominal7 unique values
0 missing
Site.Namenominal53 unique values
0 missing
Environment.Typenominal4 unique values
0 missing
Zonenominal30 unique values
0 missing
Altitude..m.numeric41 unique values
0 missing
PM.sub.2.5..sub..particulate.matter..Hourly.measured.numeric16605 unique values
0 missing

62 properties

394299
Number of instances (rows) of the dataset.
10
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
4
Number of numeric attributes.
5
Number of nominal attributes.
17.7
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
3.21
Second quartile (Median) of skewness among attributes of the numeric type.
Number of instances belonging to the most frequent class.
-1.21
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
10.55
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
9.66
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
67.73
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
65.83
Third quartile of kurtosis among attributes of the numeric type.
45.32
Maximum of means among attributes of the numeric type.
4
The minimal number of distinct values among attributes of the nominal type.
40
Percentage of numeric attributes.
37.71
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
0
Minimum skewness among attributes of the numeric type.
50
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
53
The maximum number of distinct values among attributes of the nominal type.
6.94
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
4.08
Third quartile of skewness among attributes of the numeric type.
4.18
Maximum skewness among attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
1.72
First quartile of kurtosis among attributes of the numeric type.
35
Third quartile of standard deviation of attributes of the numeric type.
42.75
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
10.12
First quartile of means among attributes of the numeric type.
20.44
Standard deviation of the number of distinct values among attributes of the nominal type.
34.29
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
20.35
Mean of means among attributes of the numeric type.
0.66
First quartile of skewness among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
7.54
First quartile of standard deviation of attributes of the numeric type.
-2
Average class difference between consecutive instances.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.
Entropy of the target attribute values.
21.2
Average number of distinct values among the attributes of the nominal type.
35.33
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
2.65
Mean skewness among attributes of the numeric type.
13.2
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.

8 tasks

0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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