RatWatch: Week 10

It’s the final week! What an adventure this summer has been! From working with community members to building tools that have real world impact, it’s been an absolute blast. This last week has been nothing short of busy. We are making final adjustments to the app, website, and statistical model, all while writing up documentation for those who will continue working on the project after this summer. In addition, we had the chance to meet with city officials and present our work to them last Friday. They showed interest in our project, and we plan to continue conversations with them on how we can use our work to help the city.

As we finish these last few days at Georgia Tech, we wanted to say thanks to all of our project advisors (Ellen, Carl, Amanda) and the entirety of the CDS program. Also, a big congratulations to the other teams on the progress they made and their continued support throughout the program. We’ve learned a lot from each other and created friendships that will last a lifetime. In summary, it’s been an invaluable experience to the entire RatWatch team! We got the chance to see what research was really like, and more importantly, how it can have a positive impact on the real world. It’s an experience that will stay with each team member for the rest of their lives. With that, we’d like to say a final thank you and farewell. It’s been a great ride, and we’ll always cherish it for the years to come. RatWatch team signing off…

RatWatch: Week 9

Presentations are this week! The RatWatch team is diligently preparing for the final presentation, getting powerpoint slides ready and finalizing the remainder of the project. We have officially finished the website, with the heat map and model fully integrated. We’ve also simplified the design of the website to make it easier to use. A navigation menu was also added to make it easier to find certain sections of the website. We are excited to show you the final product! Expect an official launch next week!

We’ve also taken the time to rethink our approach to the project using what we learned last week from community members. In addition to simplifying the survey and the website, we also plan to advance our advertising efforts to spread the word about RatWatch even further. We want to make sure we reach out to all communities to make sure they are aware of the project and our efforts to help with rat abatement and capture. It’s been 9 weeks of hard work. Let’s finish strong!

Atlanta Map Room – Week 8

Muniba – I continued working on the iPad Controller, primarily focusing on writing methods to calculate geographical coordinates to send to the Projector in order for it to know which area of the map to display. These points were found by first finding the pixel coordinates on the html page, and then unprojecting it to geographical coordinates using Mapbox GL functionality. The points I calculated were the four bounding points, as well as the “center” of the leftmost and rightmost projections of the selected map. As well as this, I worked with Melanie to get our application on an Ubuntu virtual machine so we no longer have to run locally.

Annabel – I spent a good chunk of this week working with Muniba and Chris on the interface of the Map Room. We want the user to be able to highlight specific points in the data for discussion, so I created a page that allows you to select from the currently visible points and have that point highlighted on the map, accompanied by its available information on the panel. Besides that, I’ve been spending a bit of time trying to figure out the irregularities (or, rather, unexpected numbers) in the tax assessment data; Amanda looked over the dataset with me and narrowed down the unexpected numbers into a shorter time span and Dr. Dan Immergluck of GSU was then able to enlighten us as to why that change was happening.

The screen to select a point from those currently on view:

The dynamic panel on the projector screen (featuring a highlighted point)

Seeing Like A Bike: Week 8

Over the last week we have made a large amount of progress! Last Tuesday we did 8 test runs around piedmont park and got full coverage from one of our sensors on every run and coverage on 5 out of 8 runs with the other sensor. From there, the sensors have only gotten more reliable, and we have stopped getting empty readings at points throughout the data. With the sensors acting more predictably and the GRIMM back we have been focusing this week on collecting and analyzing data. In addition, we have added a second route, this time around Georgia Tech so as to increase the speed at which we can complete runs. The wealth of data has allowed us to begin seeing trends in how the sensors relate to each other, and given us lots of ideas for test runs to make over the coming week.

The above graph shows one of our runs at piedmont park where we had one sensor placed near the GRIMM, represented by the red line, at the front of the steel bike and one sensor on the front of the pink bike, which is represented by the blue line.  The GRIMM readings are the grey line. We were able to confirm that the spikes in the GRIMM readings are real data, and not mistakes caused by the turbulence of a bike ride. While neither sensor shows such large spikes, they are close to the GRIMM’s readings in general.

Next week will take us even closer to the end of the program and we intend to completely finish data collection before Monday so we can devote the week to data analysis and preparation for presentations!

RatWatch: Week 8

Yesterday, we had a meeting with community partners from the Westside where we presented our work on RatWatch thus far. The focus was on the usage of the app, both from an individual aspect and a community aspect. Users generally did not have a problem using the app, although some preferred to send images first. However, the bigger concern was actually engaging people to text in when they did see a rat, especially on the westside where there were significantly fewer reports were than on the eastside of Atlanta.

As RatWatch is meant to be a tool for data collection that eventually leads to advocacy, a lack of usage and thus a lack of data presents a challenge. The focus on the modeling side is now to analyze specific code violation and building permit data in a concentrated area to see whether targeted action towards a certain type of code violation affects rat prevalence. This is difficult, however, with the sparsity of data on rat sightings that we currently have, so extrapolation may be necessary.

ATL Map Room: Week 7

Muniba- For the mapping interface, we’re currently working with Melanie from support to switch over and run off of an Ubuntu virtual machine rather than locally off our laptops. I’ve created a toggle for the rectangle in the Controller, so users can choose between a full size, 16 foot map and a half-sized map, and integrated the toggle with Socket.io so that information is sent across the server to the Projector. Also, for the Controller on the iPad, I’m working on creating a faded square that shows the user which portion of the map is currently being projected. Moving forward into next week, we plan to complete the prototype of our Atlanta Map Room so that in the week after, we can have our first set of participants from Dr. Loukissas’s class.

The image above shows the updated set up for the Atlanta Map Room – we have a long, 16 foot platform for participants to draw their maps on. You can also see a sample map of Atlanta traced by our project manager, Chris, using our projector interface. The drawing robot was sent to us from the St. Louis Map Room, however we are not yet sure what role it would play.


Annabel – At the beginning of this week I finished up the final map for the tax assessment data as well as a version 2.0 for the panel. After assessing the two in combination, Dr. Loukissas pointed out that it might be more helpful to see the raw data, rather than an explanation of it, next to the mapped data. I’ve subsequently been working on, for most of this week, a dynamic table that shows the all attributes for a given address, which help to understand a point on the map in context. Right now I’ve got the table dynamically updating to the current bounds of the map – via Node.js and the DataTables jquery plug-in – but it needs a hefty bit of stylistic overhaul before it can be seen in the light of day!

Some snapshots of the tax assessment map, below:

Most of Fulton County

On a smaller scale

RatWatch: Week 7

We have finished our data collection period! We managed to gather about 76 reports in total, 7 of which are evidence reports, with the rest being sightings. InImage result for rat 4th of july cartoon addition to the reports themselves, we also gathered some really interesting insights on the data collected. This includes observing how users respond, what times they tend to make reports, and how they interact with the overall application. Through this, we’ve been able to make some pretty substantial changes to the app that we hope will improve the user experience, make data collection more efficient, and enhance its overall effectiveness. We are still working very diligently to develop and implement the new features and factors into the app and statistical model. It’s an arduous process, but we are making great progress. We’ll have more to share next week! Until then, happy 4th of July!

Seeing Like a Bike: Week 7

Our progress over the last week was fairly straightforward, and we are currently on track to have great results by the end of the program. We spent most of the last week without the GRIMM, so we didn’t go out to collect more data. However, we started preliminary analysis of the data we collected last Monday, testing to see if our method of mobile air quality sensing is feasible.

When the real-time clocks arrived, we connected them to the Raspberry Pi, and updated our code to take advantage of the new hardware. This was a very important step, as Pi’s themselves don’t contain a clock on-board, so when the Pi is powered off, it doesn’t actually record the passage of time, adding a significant layer of confusion onto our data collection.

In the meanwhile, Urvi has been designing a 3d printed box to place the Arduino and Air Quality sensor inside, which should hopefully mitigate the many problems we have been having with wiring. The biggest issue is simply that our current setup is only temporary, a few weeks at most, so we don’t want to take the step of soldering the wires to the Arduino. Instead, we are using hot-glue, and electrical tape, which is holding up decently, but not as reliably as we would like.

On Friday, I went out for the first time with two sensors simultaneously. Instead of going on our typical 2.5 mile route starting from Piedmont, I simply took a short loop around Georgia Tech’s campus. These results were promising, but one of our two sensors was giving us very inconsistent data, as around 10% of the time, it would just decide to not give us any data, and we couldn’t find a specific cause of this. Also on Friday, we got the GRIMM back, and to make up for lost time, I decided to go out again on Saturday to do more tests.

On Saturday, I spent the beginning part of the day setting up the entire system, and finalizing the hardware for the bike, and software for the Pi. After going for another quick ride around campus, I discovered that some of our wiring had failed on that run. The next few hours, I focused on rewiring the system, and ensuring that the system would be reliable and resistant to stress. On the second run that day, the setup worked perfectly! Additionally, the data we collected exceeded our expectations, as the two sensors seemed to be aligned perfectly with each other, and seemed to differentiate the various types of streets on the route very well. On the first three segments, the values mimicked each other, and on the 4th segment, the variance in values was significant, but easily explainable due to the positioning of the sensors on the bike (see above image), and the construction taking place on the route. In fact, we would be more worried if the data did match on the last segment!

After analyzing the data from Saturday, we went on another run today, to collect more data similar to our standard Piedmont route choice, as well as adding GPS data collected from a log on my phone. Tomorrow, we plan to have our final major data-collection day, as we will hold experiments not just on the feasibility of the system, but to collect actual, usable data! The rest of the week will be spent analyzing the data from tomorrow, and deciding our path for the last three weeks of the program.

Electric Vehicle Infrastructure: Week 6

This week was incredibly productive for Team EV. While we wait for our IRB to be approved we are happy to put survey design aside for a few days while we are hard at work wrapping up our sentiment analysis.

The final model, a convolutional neural network, is complete, thanks to Kevin’s hard work. We are now just adding new features to see if it further increases the model’s accuracy. Once our best model is complete, we will use it to get the most accurate results possible.

Meanwhile, Arielle and Emerson have worked on analyzing the sentiment results using the classifications from the (slightly less accurate) support vector machine. Once the CNN is completely done, their code will be rerun with the more accurately classified data. Emerson created an interactive map in Leaflet and D3 that allows users to visualize and inspect any charger location in North America. The map will help identify possible trends in the data that can later be investigated for statistical significance. Arielle has been working on creating models in R to figure out what factors are associated with a location having more positive or negative sentiment. While results are still preliminary, it seems as though the day of the week is a predictor for whether or not a location will have more negative reviews!

Lastly, we are preparing a working paper for the Bloomberg Data for Good Exchange! Our abstract is due this Sunday and the paper next week so we are writing away to submit our best work.

By next week, we hope to have our IRB approved so that we can put up our surveys as soon as possible!

A mapping of EV station reviews in the LA metro. Larger circles signify more reviews and greener reviews signify a more positive sentiment in reviews for the station

Atlanta Map Room: Week 6

Muniba – This week, I primarily worked on continuing to develop the mapping application for the Atlanta Map Room. Currently, users are able to zoom, rotate, and toggle layers for our map through our Controller interface on the iPad, which shows a static, rectangular “window” of the area which will be displayed. Then, using Socket.io, these events are emitted to our local server, which in turn pushes that information to the Projector interface to display.

Image above shows our Project Manager, Chris, drawing a map projected from our interface. Above, you’ll see the rail on which our projector slides across.

Annabel: I spent the majority of this week finishing up geocoding the tax assessment data, which I’ve found really interesting as a case study in civic data. There are a lot of irregularities which make it difficult to handle the set, in my opinion – for example, Sidney Marcus Boulevard is alternately referred to as “Sidney Marcus Blvd” and “Sidney Marcus Blv” which creates a bit of an issue when you need to extract the core portion of the street name, but my regex skills are getting a good workout! I’ve also been finalizing the visualization for the tax assessment data; I’m currently working on making the color intensity proportional to the percent change in assessment from 2010 – 2017/18. A small sneak peek is here, with more to come next week: