CalIt2 Building People Counts
Donated on 11/30/2006
This data comes from the main door of the CalIt2 building at UCI.
Dataset Characteristics
Multivariate, Time-Series
Subject Area
Other
Associated Tasks
-
Feature Type
Categorical, Integer
# Instances
10080
# Features
-
Dataset Information
Additional Information
Observations come from 2 data streams (people flow in and out of the building), over 15 weeks, 48 time slices per day (half hour count aggregates). The purpose is to predict the presence of an event such as a conference in the building that is reflected by unusually high people counts for that day/time period.
Has Missing Values?
No
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| no | |||||
| no | |||||
| no | |||||
| no |
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Additional Variable Information
1. Flow ID: 7 is out flow, 9 is in flow 2. Date: MM/DD/YY 3. Time: HH:MM:SS 4. Count: Number of counts reported for the previous half hour Rows: Each half hour time slice is represented by 2 rows: one row for the out flow during that time period (ID=7) and one row for the in flow during that time period (ID=9) Attributes in .events file ("ground truth") 1. Date: MM/DD/YY 2. Begin event time: HH:MM:SS (military) 3. End event time: HH:MM:SS (military) 4. Event name (anonymized)
Dataset Files
| File | Size |
|---|---|
| Dodgers.data | 938.9 KB |
| CalIt2.data | 218.2 KB |
| Dodgers.events | 4 KB |
| Dodgers.names | 2 KB |
| CalIt2.names | 1.7 KB |
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Reviews
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset calit2_building_people_counts = fetch_ucirepo(id=156) # data (as pandas dataframes) X = calit2_building_people_counts.data.features y = calit2_building_people_counts.data.targets # metadata print(calit2_building_people_counts.metadata) # variable information print(calit2_building_people_counts.variables)
Hutchins, J. (2006). CalIt2 Building People Counts [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5NG78.
Creators
Jon Hutchins
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.