Washington DC Crime Shiny App

Posted on Sep 4, 2017

2012 -2017 Washington DC Crime Report


The capital city of the United States of America, Washington DC, does not have a very good reputation for public safety in the history. This shiny app is focused on giving the user information about Washington DC’s crime from 2012-2017(July 13th).

As an International Student, I know my personal safety is always the priority concern for family and friends at my home country. This shiny app can also provide information to newly arrived International students in search of a safe area in which to live.


I created the following section to better analyze and visualize the data.


Total Crime with Different Shift

This Bar Chart shows total crime recorded from different shifts: morning, night and midnight. The user can choose different years to see the change of the quantities of crime that happened on different shifts. Although the specific time range about morning, night and midnight are not provided from the data source. We still can observe a relatively low quantity of crime during midnight time range.

Total Crime with Different Crime Type

The Bar Chart gives information about different types of crime that usually occur in the Washington DC area. Assault is a defined as an assault with dangerous weapon or caused serious injury. Theft F/Auto include stealing motor vehicle parts and anything inside of the motor vehicle.  

Total Crime with Different District

It is very important to find out which area is relatively dangerous and which area is a good place to stay. The Bar Chart provides some information about each district’s total crime quantities.


The cluster map is clearly giving information about the number of recorded crimes on every street. It intuitively shows valuable information for the user.

The Density Map

The density Map provides another vision of the location of crimes. The user can select different crime types to see which is most common in a particular area.

The Red Zone

The red zone defined as top 10 repeated crime location.  This map plot is created meant to warn for certain area that people might get a higher chance to be a victim of a crime.

The Holiday Plot

Crimes are said to escalate during holidays. The definition of holidays for this plot is New Year, Independence Day, Thanksgiving, and Christmas. The result actually shows the total number of crimes occurring during holidays is a relatively minuscule percentage of the total number of yearly crimes.

Growth Rate

The growth rate shows the total quantities of crime is slightly declining since 2014.


This shiny app can give the newly arrived international student a very simple and clear information about Washington DC’s public safety situation. It is a very helpful tool for them when choosing a place to live.





Code: https://github.com/nash13cxw/1stshiny

About Author

Xiaowei Cheng

Xiaowei holds a Bachelor’s degree in Finance and now is pursuing his Master’s in Biostatistics at George Washington University. Xiaowei discovered his passion in data through research projects in graduate school, where he worked with professors on analyzing...
View all posts by Xiaowei Cheng >

Related Articles

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 airbnb Alex Baransky alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus API Application artist aws beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep Bundles California Cancer Research capstone Career Career Day citibike clustering Coding Course Demo Course Report D3.js data Data Analyst data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization Deep Learning Demo Day Discount dplyr employer networking feature engineering Finance Financial Data Science Flask gbm Get Hired ggplot2 googleVis Hadoop higgs boson Hiring hiring partner events Hiring Partners Industry Experts Instructor Blog Instructor Interview Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter lasso regression Lead Data Scienctist Lead Data Scientist leaflet linear regression Logistic Regression machine learning Maps matplotlib Medical Research Meet the team meetup Networking neural network Neural networks New Courses nlp NYC NYC Data Science nyc data science academy NYC Open Data NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time Portfolio Development prediction Prework Programming PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn Selenium sentiment analysis Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau team TensorFlow Testimonial tf-idf Top Data Science Bootcamp twitter visualization web scraping Weekend Course What to expect word cloud word2vec XGBoost yelp