Term: Spring 2018
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Team #6
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Rent Easy in Manhattan: Team members:
- Yuexuan Huang (Presenter)
- Wenyuan Gu
- Anshuma Chandak
- Zhongxing Xue
- Weibo Zhang
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Project summary:
Here is the link to our RShiny Application: https://ads-spr2018-proj2-grp6.shinyapps.io/SecondApp/
People have described Apartment Hunting as a fate worse than Death. Do you remember your days when you were back in your home-country anxiously looking for an apartment that is affordable and safe? Inspired by every student's problem, we made an application that will help a renter find a room based on top two priorities- price and safety. Our application will provide a comprehensive view of the rent prices and crime rate in every street in the Manhattan Borough of New York City.
The application is divided into four sections- a query map where the user can feed in their choices, and get all the apartment options categorized by crime rate and apartment price in their desired area. We have included information about the nearest police station, and subway. Second section has A Crime plot map ,which shows the distribution of crime in Manhattan categorized by 3 crimes- Felony, Misdemeanor, and Violation. In the third section, we have rent heat map where KNN methods are used to split Manhattan area in to 1850x1500 cells.In the fourth section, we summarize our study by providing several insightful charts about the categories of crimes committed, and rent prices. We have used NYPD Complaint Data (to get crime related data), and Manhattan Housing Data from www.data.gov .
Crimes receive different classifications according to their severity. The mildest crimes are known as violations, more serious crimes are known as misdemeanors, and the most serious crimes are known as felonies.A violation, sometimes called a petty offense, is the violation of an administrative regulation, an ordinance, a municipal code, and, in some jurisdictions, a state or local traffic rule. Misdemeanors are those crimes punishable by imprisonment for a year or lesser.Felonies are generally punishable by a fine, imprisonment for more than a year, or both. At common law, felonies were crimes that typically involved moral turpitude, or offenses that violated the moral standards of the community.
The above plot shows that Misdemeanor has the highest number of cases, followed by Felony and Violations.
Under Misdemeanor, we observe that assaults constitute the highest share of crimes followed by criminal mischief and drug related crimes.
The above plot shows the relationship between crime frequency and the rent. We notice that areas with high rent prices have a lower incidence of crime. However, Chelsea and Clinton area seem to be an abnormality as both the rent prices and crime frequency are high.
We also observe that crime in Manhattan is fairly evenly distributed with higher incidence of crime in Midtown area (towards the West Side), and Uptown Area.
A few screenshots from our map to give you a sneak peak in to how it can help you!
The following is the query map interface
The following is the Crime pie plot showing the distribution of crimes in Manhattan
- Contribution statement: (default)
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Zhongxing Xue: Zhongxing proposed initial idea of Crime Topic and scheduled the time and mission for whole team. He helped cleaning the rent data and took responsibility of the Query map. He set up the whole initial environment frame of Shiny App, integrated text, codes and plots together, and made it alive.
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Anshuma Chandak: Anshuma kept track of the team's activity, and set the project timeline. She updated the Readme file with project summary and contribution statements. She helped clean Crime data, worked on Crime pie map and pie plots related to crime data, as well as the code for text integration for each pie plot. She helped in customization of the home page.
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Yuexuan Huang: Yuexuan is responsible for the presentation of the project. She helped in cleaning the Crime data set, designed the home page and crime summary page, made the crime pie map and crime summary plots with Anshuma.
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Wenyuan Gu: Wenyuan proposed the idea of the relationship between rent price and crime. He helped clean the rent data and made the crime & rent summary plot.
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Weibo Zhang: Weibo proposed the idea of using Heatmap to show the housing price as more continuous spatial data and generating location data by KNN methods and visualization.
Following suggestions by RICH FITZJOHN (@richfitz). This folder is orgarnized as follows.
- Reference:
- Shiny_tutorial: https://github.com/TZstatsADS/ADS_Teaching/blob/master/Tutorials/wk4-Shiny_tutorial/Shiny_tutorial.Rmd
- Manhattan Off-Campus Housing: https://fall17grp6proj2.shinyapps.io/shinyrent1/
proj/
├── app/
├── lib/
├── data/
├── doc/
└── output/
Please see each subfolder for a README file.