Difference between revisions of "Group14 Proposal"
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In 2016, there were 1.459 billion departures made by international outbound tourists from their country of usual residences, which was an increase of 46% from the 999 million departures in 2006, in a span of 10 years.[1] | In 2016, there were 1.459 billion departures made by international outbound tourists from their country of usual residences, which was an increase of 46% from the 999 million departures in 2006, in a span of 10 years.[1] | ||
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Therefore, this project aims to create a visualisation of the spatial relationship between crime and hospitality, and to delve deeper into understanding the difference in the relationship between crime and the conventional hospitality business – Hotels and Hostels, and between crime and Airbnb. Additionally, this project will drill down into visualizing the different types of crimes that are most spatially related to the different categories of hospitality business. | Therefore, this project aims to create a visualisation of the spatial relationship between crime and hospitality, and to delve deeper into understanding the difference in the relationship between crime and the conventional hospitality business – Hotels and Hostels, and between crime and Airbnb. Additionally, this project will drill down into visualizing the different types of crimes that are most spatially related to the different categories of hospitality business. | ||
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To ensure a sustainable development of the tourism industry by decreasing the levels of crime and victimization of tourists should be of the utmost concern of policy-makers. We hope that by initializing the visualization of the effects of tourism on crime and public disorder on a local level, greater awareness towards this problem will be achieved, and interoperability between local governmental agencies will be encouraged to address this issue. | To ensure a sustainable development of the tourism industry by decreasing the levels of crime and victimization of tourists should be of the utmost concern of policy-makers. We hope that by initializing the visualization of the effects of tourism on crime and public disorder on a local level, greater awareness towards this problem will be achieved, and interoperability between local governmental agencies will be encouraged to address this issue. | ||
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Through our analysis, we hope to address the following: <br /> | Through our analysis, we hope to address the following: <br /> | ||
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'''3) Analyse Crime patterns by Day versus Night Crime; Weekday versus Weekend Crimes between Hotels and Airbnb'''<br /> | '''3) Analyse Crime patterns by Day versus Night Crime; Weekday versus Weekend Crimes between Hotels and Airbnb'''<br /> | ||
For example, find out if certain crimes are more prevalent during the weekend compared to weekdays. | For example, find out if certain crimes are more prevalent during the weekend compared to weekdays. | ||
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Revision as of 23:14, 15 June 2018
Exploring how Geospatial Relationships affects Crime in Airbnb versus Hotels
Contents
Overview
In 2016, there were 1.459 billion departures made by international outbound tourists from their country of usual residences, which was an increase of 46% from the 999 million departures in 2006, in a span of 10 years.[1]
<Insert graph: World Bank’s Widget> <iframe src="https://data.worldbank.org/share/widget?end=2016&indicators=ST.INT.DPRT&start=1995" width='450' height='300' frameBorder='0' scrolling="no" ></iframe>
Additionally, the tourism industry has contributed significantly to the world’s economy, where the recorded expenditure of international outbound tourists in other countries amounted to USD 1.362 trillion[2].
<Insert graph: World Bank’s Widget> <iframe src="https://data.worldbank.org/share/widget?end=2016&indicators=ST.INT.XPND.CD&start=1995" width='450' height='300' frameBorder='0' scrolling="no" ></iframe>
A key factor that has contributed significantly to the tourism industry is the rise of the sharing economy. According to UNWTO (2017), the sharing economy is “the sharing of access to goods and services from peer-to-peer / private-to-private coordinated through community-based online services”. Despite having a business model that deviates from the conventional hospitality business strategy, Airbnb, a platform which allows peer-to-peer hosting and rental of private homes, has become a significant player in the hospitality industry.
However, we understand that there are various social and environmental implications that comes with the growing global hospitality business. Prior researches have shown that there is a relationship between crime and public disorder, and tourism[3], and tourists are often the victims of these crimes.[4]
Therefore, this project aims to create a visualisation of the spatial relationship between crime and hospitality, and to delve deeper into understanding the difference in the relationship between crime and the conventional hospitality business – Hotels and Hostels, and between crime and Airbnb. Additionally, this project will drill down into visualizing the different types of crimes that are most spatially related to the different categories of hospitality business.
Motivation
To ensure a sustainable development of the tourism industry by decreasing the levels of crime and victimization of tourists should be of the utmost concern of policy-makers. We hope that by initializing the visualization of the effects of tourism on crime and public disorder on a local level, greater awareness towards this problem will be achieved, and interoperability between local governmental agencies will be encouraged to address this issue.
Objectives
Through our analysis, we hope to address the following:
1) To determine if there is a spatial relationship between Crime and Airbnb versus hotels.
We want to find out if there is a difference in the criminal rates between geospatial areas close to hotels and airbnb.
2) To find out of the type of criminal activities/offences differ between airbnb and hotels.
3) Analyse Crime patterns by Day versus Night Crime; Weekday versus Weekend Crimes between Hotels and Airbnb
For example, find out if certain crimes are more prevalent during the weekend compared to weekdays.
Data Sources
| Type | Description | Source |
| Demographics | Detailed Aribnb Listings data for London | |
| Demographics | Detailed listing of Street-level crime data in London | https://data.gov.sg/dataset/master-plan-2014-subzone-boundary-no-sea |
| Demographics | Detailed listing of Hotels in London |
