## Inspiration

We always only have facebook events on our friends' timeline, so when you are boring, you may don't know what to do. So we wanna something to know if there are some guys party around you.

## What it does

Here is events map, we collect events information and show them on the map, you can get the details after you choose it.

## How I built it

With android studio, ask friends and search online info

## Challenges I ran into

hard to combine map with slide bar, database problem, Facebook login

## Accomplishments that I'm proud of

We made the DEMO!!!! Once you login, you directly have a map, and you can get the detail info of event which is nice and handy.nice UI

## What I learned

how to build api, how to write the android java

## What's next for Circle

we gonna find the way to collect information for Facebook instead of manually input, keep making ui looks better and easy to use.
## Inspiration

We were curious about what a community would look like from a commuter/tourist point of view: how convenient is it to navigate a neighbourhood on foot, by bike, and by transit?

## What it does

Our nwHacks project makes use of Google Maps API to provide location and travel time data and employs Plotly to visually present travel times within a region in a 3D topography map, given a mode of transportation. The project is presented on a web page featuring an interactive Dash display.

After choosing a starting point and a mode of transportation, we build a 3-D visualization of the connectedness of your region. Height on our plot corresponds to the time it takes to arrive at that destination. For example, if using the **transit** mode of transportation, you will see a lower value along train lines and higher values in hard-to-access communities.

## How we built it

* Make API calls to Google Maps, then use Plotly visualization to generate 3D topography map with respect to longitude, latitude, and travel time in a region of the given location. Develop a user interface with Dash.
* Can increase the area presented by increasing the grid size in API calls.
* This could easily be scaled up and hosted.

## Challenges we ran into

* Using Google API was a steep learning curve, but once we figured out API keys and the formatting, Google API’s were extremely useful and powerful!
* Integrating a google maps image into our Plotly visualization took some time. This was mainly due to the many moving parts and formats which needed to work together.
* Understanding the latitude and longitude coordinate system of the globe was another challenging but enriching learning experience.

## Accomplishments that we're proud of

* Made a program that works and a really cool visual to boot!

## What we learned

* We came to appreciate how useful google’s API calls can be. It was a new thing for all of us!
* We learned to create 3D visualizations with Plotly.
* We also learned that we could create web pages with interactive graphics completely in Python using the Dash framework.

## What's next for Transportation Topology

To improve the project, we can explore the following areas:

* Allow the use of multiple modes of transportation
* Use multiple layers of plots to make comparison between different modes of transportation
* Identify well-connected areas
* Rank most cycle-able/transit-able cities
## Inspiration

We wanted to provide an easy, interactive, and ultimately fun way to learn American Sign Language (ASL). We had the opportunity to work with the Leap Motion hardware which allowed us to track intricate real-time data surrounding hand movements. Using this data, we thought we would be able to decipher complex ASL gestures.

## What it does

Using the Leap Motion's motion tracking technology, it prompts to user to replicate various ASL gestures. With real-time feedback, it tells the user how accurate their gesture was compared to the actual hand motion. Using this feedback, users can immediately adjust their technique and ultimately better perfect their ASL!

![Alt Text](https://media.giphy.com/media/3ohc1ef7gHPfHTVPnq/giphy.gif)

## How I built it

Web app using Javascript, HTML, CSS. We had to train our data using various machine learning repositories to ensure accurate recognitions, as well as other plugins which allowed us to visualize the hand movements in real time.

## Challenges I ran into

Training the data was difficult as gestures are complex forms of data, composed of many different data points in the hand's joints and bones but also in the progression of hand "frames". As a result, we had to take in a lot of data to ensure a thorough data-set that matched these data features to an actual classification of the correct ASL label (or phrase)

## Accomplishments that I'm proud of

User Interface. Training the data. Working on a project that could actually potentially impact others!

## What I learned

Hard work and dedication. Computer vision. Machine Learning.

## What's next for Leap Motion ASL

More words? Game mode? Better training? More phrases? More complex combos of gestures?

![Alt Text](https://media.giphy.com/media/vFKqnCdLPNOKc/giphy.gif)