Difference between revisions of "BusinessMafia Proposal"
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BUSINESS MAFIA
| Line 46: | Line 46: | ||
{| class="wikitable" | {| class="wikitable" | ||
|- | |- | ||
| − | ! Data !! Source !! Data Type | + | ! Data !! Source !! Data Description !! Source URL !! Data Type |
|- | |- | ||
| − | | <center>Seattle Open Airbnb Data</center> | + | | <center>Seattle Open Airbnb Data</center> || <center>Inside Airbnb</center> || <center>To be included</center> || <center>http://insideairbnb.com/get-the-data.html</center> || <center>CSV File</center> |
|- | |- | ||
| − | | <center>Common Place Name (CPN)</center> || <center>City of Seattle Open Data Portal</center> || <center>CSV File</center> | + | | <center>Common Place Name (CPN)</center> || <center>City of Seattle Open Data Portal</center> || <center>A point feature class showing common place names and corresponding locations in Seattle.</center> || <center>https://data.seattle.gov/Land-Base/Common-Place-Names-CPN-/599c-9ddc</center> || <center>CSV File</center> |
|- | |- | ||
| − | | <center>King County Metro Stops</center> || <center>KCGIS Center</center> || <center>SHP File</center> | + | | <center>King County Metro Stops</center> || <center>KCGIS Center</center> || <center>On-street location where transit vehicles stop inline to pick-up and discharge passengers. It has a sign and basic service information; sometimes also a shelter with benches.</center> || <center>https://www5.kingcounty.gov/sdc/Metadata.aspx?Layer=transitstop#Description</center> || <center>SHP File</center> |
|- | |- | ||
| − | | <center> | + | | <center>City Clerk Neighbourhoods</center> || <center>Seattle.gov</center> || <center>Displays the 20 Large City Clerk neighborhood boundaries, along with their smaller neighborhood boundaries.</center> || <center>https://data.seattle.gov/dataset/City-Clerk-Neighborhoods/926y-cwh9</center> || <center>SHP File</center> |
|} | |} | ||
</br> | </br> | ||
Revision as of 10:14, 6 March 2019
Contents
Project Motivation
Airbnb has been democratic in providing its data access to the public for potential analysis. However, there is a lack of an aggregated platform to distill this mass of data into information that allow Airbnb hosts better understand the demands of the travelers coming into their city. Certain Airbnbs possess higher occupancy rates than others, the factors affecting it also differ from city to city and culture to culture. The reasons for visiting and type of travelers attracted also differ; as certain cities may attract more business travelers seeking comfort, while others attract backpackers looking for an affordable bed and breakfast accommodation.
With this in mind, our team is delving into the landscape of Seattle, Washington in United States to identity relationships and spatial patterns affecting the occupancy rate of Airbnbs in Seattle. We aim to help hosts better understand the demands of the travelers coming into their city, and how they can therefore increase their occupancy rates.
Project Objective
Through the project, we aim to:
- Identify airbnb hotspots and coldspots in Seattle
- Analyse the spatial relationships and patterns in Airbnb occupancy rate
Our Starting datasets
| Data | Source | Data Description | Source URL | Data Type |
|---|---|---|---|---|
Project Timeline
Literature Review
Our Approach
Web Application Design
Application Overview
