Entry Name: "KBSI-Madanagopal-MC1"

VAST Challenge 2017
Mini-Challenge 1

Team Members:

Karthic Madanagopal, kmadanagopal@kbsi.com

Kalyan Vadakkeveedu, kvadakkeveedu@kbsi.com

Akshans Verma, akverma@kbsi.com

Sanjeev Nookala, snookala@kbsi.com

Reuben Fernandes, rpfernandes@kbsi.com

Student Team: NO

Tools Used:

Tableau

Tibco Spotfire

Excel

Python Pandas

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

120 hours

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

Video

https://youtu.be/pnwxsGQ9DNA

Questions

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.

We first analyzed the “Lekagul Sensor Data” and extracted all the locations travelled by a car-id. Then we developed a visual interface that would help us to characterize type of visitors to the park. There are basically two main types of visitors to the park (1) regular commuters and (2) campers. The regular commuters are visitors that pass thru the park and spend approximately 20-30 min of time. Campers stay in the park for atleast a night. If we routes follow the same gates and spend approximately the same amount of time in the park and are of same vehicle type we classify them as patterns.

Figure 1 Commuter daily patterns

Figure 2 Commuters daily pattern by car type (Percentage)

Figure 1 above shows the top 10 routes taken by the commuter type of visitors to the park. The interface itself shows the number of people taken that route and how many unique days such pattern exist atleast once. The table summarizes the daily patterns taken by commuters.

Figure 3 Commuter patterns by car type

Figure 4 Chord diagram of the commuter pattern by entrance and exit

Figure 4 given below shows the top 10 patterns taken by the campers. Following are the top 5 routes taken by the campers.

Figure 5 Top 10 patterns of the campers

The third unique daily pattern we recognized are the ranger patterns. These patterns are extracted only from the car type 2P.

The most common ranger pattern we visualized occurred for 125 days which mostly covers the east side of the park as shown in the first chart of the above picture. The third and fourth pattern look the same with a minor variation of a single extract gate it visits. The 5^th and 7^th pattern also looks the same with a minor variation. We realized that none of the rangers routes cover camping-0.

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.

Following are some of the patterns we identified that appear over longer period of time.

(1) 3 times the route of “entrance1,camping2,general-gate7,entrance3” has been taken and stayed in the park for 14 days.

(2) Following were the patterns that lasted for 7 days were observed in multiple times.

(3) Following patterns were observed for period of 11-15 days

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.

Following are the unsual pattens we discovered from the visitor data

(1) Longest Camper: One of the visitor with car-id 20155705025759-63 has been in the park for more than 350 days from 06/05/2015 14:57. This visitor has never exited the park. This visitor has camped at all the camping locations on the west side of the park except camping 1. Every 28-30 days this visitor changes the camping location. This visitor went camping 1 and stayed there for only 15 minutes as against his regular pattern of staying for 28-30 days. We are suspecting this person could be Mitch, ornithology student who created this data challenge.

(2) Violator(1): As per the data description every user who exits the park needs to surrender their RFID and get a new one when they visit back. We found some visitors have reused their RDIF tags for multiple re-entries. Visitor with the car-id 20154519024544-322 has used the RFID tag for more than 100 days. He has a pattern of staying in the park for 4 days and leaving the park for 3 days. This user regularly comes to the park on Fridays between 2-3PM and leaves the park on Mondays between 12-1AM. This visitor regularly uses entrance 4 for all its reentries.

(3) Violator(2): Visitor with car-id 20150010050052-231 also reused the RDIF tag at entrance 4.

(4) Least preferred Camp: Camping1 is the least preferred camp in the park. Figure 6 shows the the unusual visitor count for camping1 relative to other camping locations.

Figure 6 Camp Density by camping locations

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)

Figure 6 shows the traffic patterns inside the park for each day in the dataset provided. The green color represents the hours of the day where the number of people were minimum and the red represent the maximum number of people. Orange represents the average number of people. It is apparent that the months of June, July, August and September were the busy months were the traffic is at its peak. Comparing the first chart (row1-column-1) of May 2015 with the last chart (row4-column-1) of May 2016, we realized there is an increase in the traffic from 760 to 847. This increase is only on the commuters and not on the campers.

Figure 7 Traffic Pattern inside the park

Figure 7 shows the increase in the number of visitors by visitor type (campers vs non campers). Campers are shown as green and non-campers are shown as blue. Increase in the traffic can significantly increase the noise and pollution inside the park which could directly affects the birds.

Figure 8 Number of Visitors by Date