Pokemon tracker in selected places using inferential statistic

Posted on Nov 7, 2016

Recently one phone game spread through the whole world, and cause a lot of interesting topic about the technique behind the game. One of the hot topic is: How to predict the pokemon spawning position?  

Here I developed an app to predict the pokemon spawning position and the probability of spawning at that position. This is the exciting news for the fans of the game, who can easily find the possible spawning position and collect the rare desired pokemon. Next let me introduce my app.

  1. Features Selection.           the data was downloaded from kaggle with 2906021 observation and 208 variables. 7 variables was selected, including the geography coordinates(longitude and latitude), the pokemon Id, name,appeared Time Of Day and the city.
  2. Methodology .                     group the data by location (longitude and latitude), and count the population of the specific pokemon appeared at that location, the circle on the map show the appeared location in history, the intensity of the circle's color and the radius of the circles proportional to the populations. So the darker and larger circle indicate higher possibility that the pokemon appear at that position.
  3. Function of the app.        In the application(see figures below), the customer can:
  • select the city         Totally 98 cities of different country and different continent are avaliable, and  more and more cities are going to added.
  • select the target pokemon.            All 143 kinds of pokemon were collected and the statistic data of the pokemon is shown in the table above the map.
  • select the type of street map.         There are 5 kinds of map to choose "HERE.hybridDay", "Stamen.TonerLines", "Stamen.Terrain", "CartoDB.Positron", "Esri.WorldImagery".
  • select the zoom level                  There are 18 levels of zoom can be choose.   The high level can be chose to show the detail of the destination, and the low to show the large area of the location and more spawn location to choose.


Figure 1 show the pokemon (Pidgey) spawn distribution and frequent at each spawn location


Figure 2 select the city: Los Angels and adjust the zoom level to 16, and this show very small range and explicit spawn location


Figure 3, using the the satellite map "Esri.WorldImagery" to search the very rare kind of pokemon(Pikachu) and investigate the surrounding of the location


Figure 4  the zoomed central park map showing  the explicit pokemon spawn location and the traffic.

Future Step:

Apply more features and machine learning , get more accurate prediction

Actually, there is one very complicated equation to generate the pokemon spawns. In the future, I am going to add more features, such as wind speed, temperature, resident population density, the local time, the moving speed, and apply the machine learning algorithm  to track the pokemon more accurate.

About Author


He got his PhD degree in Physics from City University of New York in 2013, and recently completed his post doctoral projects funded by CDMRP (Congressionally Directed Medical Research Programs, Department of Defense) and US Department of Energy,...
View all posts by leizhang >

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans 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 boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis 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 seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI