Data
Parking-Statistics-in-North-America

Parking-Statistics-in-North-America

active ARFF CC BY-NC-SA 4.0 Visibility: public Uploaded 23-03-2022 by Onur Yildirim
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  • Computer Systems Machine Learning
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ABOUT This dataset identifies areas within a city where drivers are experiencing difficulty searching for parking. Cities can use this data to identify problem areas, adjust signage, and more. Only cities with a population of more than 100,000 are included. Data Some variables to highlight: AvgTimeToPark: The average time taken to search for parking (in minutes) AvgTimeToParkRatio: The ratio between the average time taken to search for parking and of those not searching for parking in the current geohash TotalSearching: The number of drivers searching for parking PercentSearching: The percentage of drivers that were searching for parking AvgUniqueGeohashes: The average number of unique geohashes at the 7 character level (including neighbouring and parking geohashes) that were driven in among vehicles that searched for parking AvgTotalGeohashes: The average number of all geohashes at the 7 character level (including neighbouring and parking geohashes) that were driven in among vehicles that searched for parking CirclingDistribution: JSON object representing the neighbouring geohashes at the 7 character level whereby vehicles searching for parking tend to spend their time. Each geohash will have the average percentage of time spent in that geohash prior to parking. HourlyDistribution: JSON object representing the average prevalence of searching for parking by hour of day ( distribution based on number of vehicles experiencing parking problems) SearchingByHour: JSON object representing the average percentage of vehicles searching for parking within the hour PercentCar: Percentage of vehicles with parking issues that were cars PercentMPV: Percentage of vehicles with parking issues that were multi purpose vehicles PercentLDT: Percentage of vehicles with parking issues that were light duty trucks PercentMDT: Percentage of vehicles with parking issues that were medium duty trucks PercentHDT: Percentage of vehicles with parking issues that were heavy duty trucks PercentOther: Percentage of vehicles with parking issues that were unknown classification Content This dataset is aggregated over the previous 6 months and is updated monthly. This data is publicly available from Geotab (geotab.com). Inspiration As some inspiration, here are some questions: Which cities are the hardest to find parking? By joining population data externally, can you determine a relationship between a region's population and the time that it takes to find parking? Similarly, by finding external data, is there a correlation between GDP and parking times? What about average household income?

31 features

Geohashstring4750 unique values
0 missing
GeohashBoundsstring4750 unique values
0 missing
Latitude_SWnumeric2833 unique values
0 missing
Longitude_SWnumeric3153 unique values
0 missing
Latitude_NEnumeric2833 unique values
0 missing
Longitude_NEnumeric3153 unique values
0 missing
Locationstring4750 unique values
0 missing
Latitudenumeric3073 unique values
0 missing
Longitudenumeric3360 unique values
0 missing
Citystring336 unique values
0 missing
Countystring256 unique values
1002 missing
Statestring71 unique values
0 missing
Countrystring3 unique values
0 missing
ISO_3166_2string71 unique values
0 missing
AvgTimeToParknumeric3119 unique values
0 missing
AvgTimeToParkRationumeric1614 unique values
0 missing
TotalSearchingnumeric68 unique values
0 missing
PercentSearchingnumeric481 unique values
0 missing
AvgUniqueGeohashesnumeric404 unique values
0 missing
AvgTotalGeohashesnumeric705 unique values
0 missing
CirclingDistributionstring4750 unique values
0 missing
HourlyDistributionstring4750 unique values
0 missing
SearchingByHourstring4750 unique values
0 missing
PercentCarnumeric145 unique values
0 missing
PercentMPVnumeric181 unique values
0 missing
PercentLDTnumeric213 unique values
0 missing
PercentMDTnumeric96 unique values
0 missing
PercentHDTnumeric175 unique values
0 missing
PercentOthernumeric172 unique values
0 missing
UpdateDatestring1 unique values
0 missing
Versionnumeric1 unique values
0 missing

19 properties

4750
Number of instances (rows) of the dataset.
31
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
1002
Number of missing values in the dataset.
1002
Number of instances with at least one value missing.
19
Number of numeric attributes.
0
Number of nominal attributes.
0.01
Number of attributes divided by the number of instances.
61.29
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.
21.09
Percentage of instances having missing values.
Average class difference between consecutive instances.
0.68
Percentage of missing values.

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