Entry Name: UIC-Marai-MC1

VAST Challenge 2017
Mini-Challenge 1

Team Members:

Dimitar Kirilov, University of Illinois at Chicago, dkiril4@uic.edu, PRIMARY

Isabel Lindmae, University of Illinois at Chicago, ilindm2@uic.edu

Andrew Burks, University of Illinois at Chicago, aburks3@uic.edu

Chihua Ma, University of Illinois at Chicago, cma6@uic.edu

Liz Marai, University of Illinois at Chicago, g.elisabeta.marai@gmail.com

Student Team: YES

Tools Used:

D3 JS

Bootstrap

Excel

Google Chrome

Approximately how many hours were spent working on this submission in total?

100+

May we post your submission in the Visual Analytics Benchmark Repository after VAST Challenge 2017 is complete? YES

Video: https://youtu.be/faTT306CEeY

Question 1 – “Patterns of Life” analyses depend on recognizing repeating patterns of activities by individuals or groups. Describe up to six daily patterns of life by vehicles traveling through and within the park. Characterize the patterns by describing the kinds of vehicles participating, their spatial activities (where do they go?), their temporal activities (when does the pattern happen?), and provide a hypothesis of what the pattern represents (for example, if I drove to a coffee house every morning, but did not stay for long, you might hypothesize I’m getting coffee “to-go”). Please limit your answer to six images and 500 words.

Pattern 1: We created a web-based visual analysis tool that links sortable, zoomable heatmaps and stacked graphs, along with filters, and that can be drilled down on. Our first stacked histogram shows time spent in the park vs number of vehicles. We can see that only 2 Axle Cars / Motorcycles, 2 Axle Trucks, and 3 Axle Trucks stay more than a day in the park preserve. This indicates that the people who stay overnight in the park are probably families. If campers stay, then they only move around the park on foot.

Pattern 2: Looking at the general hour vs gate heatmap, we can see that the most vehicle activity is during the day between 7am and 4pm, which makes sense since that's when the general public goes outdoors to the parks.

Pattern 3: By sorting the daily heatmap by busiest sensors (each clustered its own group), we can see that general gates and ranger stops receive the most activity. Also, it appears that ranger stops 0 and 2 receive significantly more activity than the rest of the sensors.

Question 2 – Patterns of Life analyses may also depend on understanding what patterns appear over longer periods of time (in this case, over multiple days). Describe up to six patterns of life that occur over multiple days (including across the entire data set) by vehicles traveling through and within the park. Characterize the patterns by describing the kinds of vehicles participating, their spatial activities (where do they go?), their temporal activities (when does the pattern happen?), and provide a hypothesis of what the pattern represents (for example, many vehicles showing up at the same location each Saturday at the same time may suggest some activity occurring there each Saturday). Please limit your answer to six images and 500 words.

Pattern 1: The first pattern that occurs over multiple days is about vehicles that stay over multiple days (perhaps for camping purposes). We created a histogram of the amount of vehicles (y axis) vs  total time spent range (x axis). By looking at this it's evident that there are many people who stay overnight in the park and upon closer inspection on the day vs vehicles view, we see that there is a gradual decline for the amount of days people spent in the park.

Pattern 2: A second pattern over multiple days is that the park preserve is busiest on Fridays, Saturdays and Sundays. By analyzing our heatmap which is ordered by day and gate location, it is evident that those days get the most activity.

Pattern 3: Ordering the data by the busiest day and inspecting the date reveals that July is the busiest month of the park preserve.

Pattern 4: Looking at the general heatmap sorted by day, it's clear that more people go to the park preserve during the summer and there is minimal activity during the winter.

Pattern 5: There is increased car activity around the time of holidays (e.g. 4th of July)

Activities of days leading up to the 4th:

It clear that more and more people show up the closer we get to the holiday and some even stay after.

Question 3 – Unusual patterns may be patterns of activity that changes from an established pattern, or are just difficult to explain from what you know of a situation. Describe up to six unusual patterns (either single day or multiple days) and highlight why you find them unusual. Please limit your answer to six images and 500 words.

Pattern 1: The first unusual pattern we found is one vehicle staying in the park for a long period of time. We created a histogram of the amount of vehicles (y axis) vs total time spent range (x axis). The general overview shows that there are 6 people who are staying for longer than a month and haven't left the park preserve. Upon clicking the bin, a closer inspection view shows that there are several people that have stayed for more than a month, a 2 Axle Truck that has been there for nearly 2 months, and a 2 Axle Car / Motorcycle that has been in the park preserve for nearly a year. The most notable one is the vehicle that has been in the preserve for a year and has a vehicle with id: "20155705025759-63"

Pattern 2: The second unusual pattern is on the opposite spectrum - people that have stayed for too little of time in the park preserve. Upon inspecting the 0 - 30 min bin, we are able to see that there are around 25 vehicles that have stayed in the park for less than 7 minutes. Upon clicking the bin here, we are also able to view the car ID's that show this behavior. It is possible these vehicles are using the park as a shortcut to major roads.

Pattern 3: The third unusual pattern is shown via a different filter for the histogram, which graphs the number of vehicles (y axis) vs the number of entrances they have been detected at (x axis). People that have only been to 2 entrances over their whole route are darkened.

People that have been detected by 2 entrances and are darkened give us the first unusual pattern. This means that people are cutting across the park to possibly avoid traffic as their whole route consists of entering the park and leaving it.

Pattern 4: A similar filter to the one used above shows the next unusual pattern. This time we highlight people that have been detected at more than 2 entrances. This seems unusual, as normally people are assigned an id upon entering the park and then they are supposed to give it up when they exit the park preserve. From this view it is evident that there are 11 people who don't do that, which indicates that they find a way to bypass this and avoid paying the fee to re-enter the park.

Question 4 –– What are the top 3 patterns you discovered that you suspect could be most impactful to bird life in the nature preserve? (Short text answer)

By analyzing our patterns we find some are more impactful than others to the bird life in the park preserve. First, we see that there is almost little to no activity in the park during the winter months, thus indicating that it's a colder climate. (This is also supported by MC3's images). During the spring and summer activity is booming, which could disturb the birds’ life.

Another pattern is that there are a lot of trucks and buses travelling along the park. These vehicles emit a lot of Carbon Dioxide which could be harming the bird's health. This especially holds if there are bird nests near the entrances or general gates of the park preserve.

NOTE:

Finally, the third pattern we noticed while analyzing our data was that there were 4 axle trucks detected near regular gates which should only permit park preserve vehicles normally. The data descriptions mention that these gates usually block traffic off because park rangers are doing construction, so those trucks could be that. This means that there is a lot of construction going on, which could mean that they are cutting down trees to make space, which could be extremely harmful to bird life.