Dark Mode
Web Mined Food Delivery Data
E-commerce & Online Transactions
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
Data from the research project "A Web Mining Approach to Collaborative Consumption of Food Delivery Services" offers detailed insights into food delivery operations. It includes information on service providers, delivery metrics, and traffic conditions, making it valuable for analysing urban transportation, e-commerce, and consumer behaviour in the food delivery sector.
Columns
- Moment: The time of day, categorised as Morning, Noon, or Other.
- Label Count: A numerical count value, ranging from 1 to 871.
- web: The URL of the food provider's webpage.
- Name of Provider: The name of the food delivery service provider.
- Number of Comments: The total count of comments or reviews for the provider.
- Expected Delivery Time: The estimated time for delivery, with '45' being a common value.
- Minimum Charge Ordering: The minimum order value required for delivery.
- Cost Delivery: The fee charged for the delivery service.
- Latitude: The geographical latitude of the provider.
- Longitude: The geographical longitude of the provider.
- Typical Traffic Afternoon: Traffic conditions in the afternoon, categorised as 'Orange', 'Green', or other.
- Typical Traffic Noon: Traffic conditions at noon, categorised as 'Orange', 'Green', or other.
- Typical Traffic Morning: Traffic conditions in the morning, categorised as 'Green', 'Orange', or other.
- DailyTraffic: A combined code representing daily traffic patterns (e.g., 'OOG').
- ClientLatitude: The geographical latitude of the client.
- ClientLongitude: The geographical longitude of the client.
- Distance(mts): The distance between the provider and the client in metres.
- Time(sec): The delivery time in seconds.
- Time(min): The delivery time in minutes.
Distribution
The data is provided in a single CSV file named
newdata.csv, with a size of approximately 3.5 MB. It is structured into 19 columns and contains 19,900 records.Usage
This dataset is ideal for a variety of applications, including:
- Data Visualisation: Creating maps and charts to illustrate delivery patterns, traffic impact, and service provider distribution.
- Urban Transportation Analysis: Studying the relationship between traffic conditions and delivery times to optimise logistics.
- E-commerce and Business Strategy: Analysing consumer behaviour, provider performance, and market dynamics to inform business decisions.
- Predictive Modelling: Developing models to forecast delivery times based on distance, time of day, and traffic.
Coverage
The geographic scope of this dataset focuses on Bogotá, as indicated by an example URL within the data (
https://domicilios.com/bogota/mcdonalds-1-de-mayo.html#menu). The specific time range and demographic coverage are not specified.License
CC0: Public Domain
Who Can Use It
- Data Scientists and Analysts: For exploring patterns, building predictive models, and performing statistical analysis on urban logistics.
- Urban Planners: To understand transportation dynamics and the impact of e-commerce services on city infrastructure.
- Business Strategists: For market research, competitor analysis, and optimising food delivery operations.
- Academic Researchers: To study collaborative consumption, web mining applications, and consumer behaviour.
Dataset Name Suggestions
- Bogotá Food Delivery and Traffic Analysis
- Collaborative Consumption in Food Delivery Services
- Urban Logistics and E-Commerce Metrics
- Web Mined Food Delivery Data
Attributes
Original Data Source:
QUALITY