PokemonGoers/PokeMap-1

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Apache License 2.0
Created over 8 years ago
Updated almost 8 years ago

PokemonGoers/PokeMap-1

PokemonGoers

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The world of Pokemon GO is as big as our planet. Pokemons have been sighted on top of cliffs perched over oceans as well as in your next door coffee shop. We would like to create a world-wide interactive map that shows where Pokemons were predicted to appear. Each pokemon prediction you add to the map should have all relevant information including name, time the pokemon is predicted to appear, prediction confidence rate etc. The map should be filtered by a time range (i.e predicted to appear in the next day) as well as pokemon name and pokemon specie.

PokeData

In this project you will scrape as much data as you can get about the *actual* sightings of Pokemons. As it turns out, players all around the world started reporting sightings of Pokemons and are logging them into a central repository (i.e. a database). We want to get this data so we can train our machine learning models. You will of course need to come up with other data sources not only for sightings but also for other relevant details that can be used later on as features for our machine learning algorithm (see Project B). Additional features could be air temperature during the given timestamp of sighting, location close to water, buildings or parks. Consult with Pokemon Go expert if you have such around you and come up with as many features as possible that describe a place, time and name of a sighted Pokemon. Another feature that you will implement is a twitter listener: You will use the twitter streaming API (https://dev.twitter.com/streaming/public) to listen on a specific topic (for example, the #foundPokemon hashtag). When a new tweet with that hashtag is written, an event will be fired in your application checking the details of the tweet, e.g. location, user, time stamp. Additionally, you will try to parse formatted text from the tweets to construct a new “seen” record that consequently will be added to the database. Some of the attributes of the record will be the Pokemon's name, location and the time stamp. Additional data sources (here is one: https://pkmngowiki.com/wiki/Pok%C3%A9mon) will also need to be integrated to give us more information about Pokemons e.g. what they are, what’s their relationship, what they can transform into, which attacks they can perform etc.

PredictPokemon-2

In this project we will apply machine learning to establish the TLN (Time, Location and Name - that is where pokemons will appear, at what date and time, and which Pokemon will it be) prediction in Pokemon Go.

Catch-em-all

Now that we have tons of data about Pokemon (what they are, where they are, what’s their relationship, what they can transform into, which attacks they can perform, aso) we want to integrate it all into a comprehensive website. This website should contain sections about each Pokemon and its details. Additionally, the website should register the user’s location and tell the user how close is that the predicted pokemon to him/her. Additionally you will be incorporating the apps that were created by project B,C and D into the website. Your group will need to create automated builds and testing for this apps and use continuous integration to pull in new changes in the code repositories. Apps from projects B-D should be packaged and made available on NPM. Ideally when you completed these tasks the webapp component would integrate the apps by “requiring’ them. Here is a possible user story: when a user opens the website or the app the current location of the user will be shown. Additionally, the website/app will show automatically where the pokemons that are currently active are and where the pokemons that we predict to active in the nearest future (i.e. within half a day) will be located (all of this will be available from the app developed in project D). Hopefully, the website will be somewhat crowded by that data. Then, there needs to be a menu bar or something available (e.g. above the map or on the right side to it) that will list currently active or predicted pokemons. Clicking on one of them will make other pokemons on the map disappear, except of this clicked one. Separate web pages would allow the search and presentation of individual Pokemons and the information we gathered about them, including third party data (project A) and twitter analysis (project C)

PokeMap-2

The world of Pokemon GO is as big as our planet. Pokemons have been sighted on top of cliffs perched over oceans as well as in your next door coffee shop. We would like to create a world-wide interactive map that shows where Pokemons were predicted to appear. Each pokemon prediction you add to the map should have all relevant information including name, time the pokemon is predicted to appear, prediction confidence rate etc. The map should be filtered by a time range (i.e predicted to appear in the next day) as well as pokemon name and pokemon specie.

HashPokemonGo

Live sentiment analysis on pokemon in a x km radius - this can be easily implemented expanding on the work done last semester https://github.com/Rostlab/JS16_ProjectD_Group5 . We also want to know what people think about that Pokemon! So the user of the app should be able to visualize a live sentiment feed around his/her area (that is, given a lat/lng and a specific radius), and be able to see if people around him/her think positively or negatively about that pokemon. Additionally, since you will become the twitter experts, you will join forces with project A to realize the live-tweet miner.