Entry Name: UCAS-ShuZhang-MC1
VAST
Challenge 2017
Mini-Challenge 1
Team Members:
Shu Zhang, University of Chinese Academy of Sciences, zhangshugar@163.com PRIMARY
Danhuai Guo, Chinese Academy of Sciences Computer Network Information Center, guodanhuai@cnic.cn
Yingqiu Zhu, University of Chinese Academy of Sciences, zyq@cnic.cn
Deqiang Wang, University of Chinese Academy of Sciences, wangdeqiang@cnic.cn
Student Team: YES
Tools Used:
Approximately how
many hours were spent working on this submission in total?
50 hours
May we post your
submission in the Visual Analytics Benchmark Repository after VAST Challenge
2017 is complete? Yes
Video
https://pan.baidu.com/s/1qXMWJOo
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.
To identify daily patterns of activities by individuals or groups, we conducted a statistic analysis based on the data of trajectories through or within the park. As pre-processsing, a topology diagram, which illustrated the adjacency relationships between locations within the park, was abstracted from the original map. The spatial structure was simplified in the topology and all monitored locations were manually annotated. Then each trajectory was denoted using a sequence consist of annotations, expressed on the topology diagram, as in shown in figure 1-1, and classified according to the type of the vehicle. Figure 1-1: the topology diagram of trajectories. A: trajectories of 2 axle cars, 2 axle trucks and 3 axle trucks. B: trajectories of 4 axle trucks, 2 axle buses and 3 axle buses. Trajectories of those vehicles did not pass any campsites. Firstly, we extracted paths, each of which occurred in numerous trajectories and described a unique route, from the data of trajectories. For each path, the number of corresponding trajectories was counted to reflect its frequency. Figure 1-2 shows the frequency of all paths. Figure 1-2: amounts of trajectories of paths A: paths that covered the 9 campsites grouped by involved campsites. In part A, each line illustrates the frequencies of different paths that passed through a same campsite and each block corresponded to a certain path. B: paths that did not cover campsites. Each line denotes the frequency of a path between two entrances of the park without passing any campsites. The high-frequency part shows that there was only one unique undirected path between any two entrances of the park (except odd paths that only occurred several times). It is intuitive that paths covered campsites were more complex and varied. For trajectories that did not pass campsites, the paths can be obviously recognized as two groups: paths with high frequency and low-frequency paths, which have been used for only several times. For part B, paths with high frequency were considered to reveal general patterns of daily behaviors. Then the topology diagram was utilized for analysis of the spatial distribution of trajectories and a sequence diagram was exploited to illustrate the temporal distribution of trajectories. The sequence diagram also shows the distribution of different types of vehicles. For a path without passing campsites, as is shown in figure 1-3, the corresponding route was unique and the spatial distribution of records was approximately uniform. For each path, most of the records had similar duration (about 20 to 70 minutes) within the park. Therefore, we assumed that there was a general pattern of behavior of individuals, namely just passing through the park to access main thoroughfares on the opposite sides of the preserve, instead of touring. The pattern fitted all types of vehicles as the records cover all the 6 types and there was not significant difference between behaviors of different types. In this pattern, passengers move along the shortest or the most optimal path to cross the area and seldom stop in the park. Interestingly, some paths showed that there were blocks around 7:00 am and 5:00 pm, which might be caused by rush hours and support our assumption. Figure 1-3: the topology diagram and the sequence diagram for paths without passing campsites A: the topology diagram. The width of a line between two nodes of the topology reflects the amount of observations passing through the sub-path among corresponding locations. Via the topological representation, we can directly find the general routes, which consist of sub-path with significant flow. B: the sequence diagram. Each line denoted a record of trajectory and presented the time when visitors spend in the park. For paths covering campsites, the result was more complicated. For each campsite, we painted topological representations of relevant paths together and illustrated all related records on a sequence diagram, as is shown in figure 4. Via the topological representations, we found that most of the vehicles were oriented to the campsite across several general gates regardless of which entrance they passed, which suggested a general behavior of normal touring. Then we analyzed the temporal distribution through the sequence diagram and identified three general patterns: Pattern 1 : One-day tour. Visitors enter the park at around 8:00 am to 4:00 pm and spend several hours (mostly around 2 hours) in the park. The corresponding trajectories mainly derived from 2 axle cars. It was assumed that visitors hang around the campsites for touring during daytime. Pattern 2 : One-day camping. Visitors enter the park at very early time (around 6:00 am to 8:00 am) and spend around 4 hours in the park, relatively longer than records of pattern 1. Those visitors consisted of drivers of 2 axle trucks, which might be caravans. We assumed that it reflected the behavior of camping within a day, which might be aimed at having enough time to enjoy the preserve or conducting special observations on animals and plants. Pattern 3 : Overnight camping. Visitors spend the night in the park and leave on the second day. Corresponding records mainly derived from 2 axle trucks and were assumed as behaviors of overnight camping with caravans. Figure 1-4: the topology diagram and the sequence diagram for paths passing campsites
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.
We calculated the sum of records for each gate in every day. The result is shown as the theme river plot, from May to October, visitor records absolutely increased and several cyclical peaks could be observed. From the conclusion of question one, we acknowledge campsite is vital and necessary for visitors to have a nice trip. We exacted the sum of records for each campsite in every day from May to October, and we used the sum of the daily records minus the average records number to explore cyclical pattern. As is shown in figure 2-1, its angle axis represents date, every grid represents a week, and its radius axis represents sum of the daily records minus the average records. Figure 2-1: Sum of records for each gate in every day From the stacked line plot figure 2-2, we found few visitors would like to go to camp since May, and starting from June, the number of people coming to camping is on the rise, and presents a weekly periodic rule, between July to August, the number of visitors going to camps has peaked regularly (every weekend), and few visitors would camp in October. Figure 2-2: Weekly periodic rule of campsite In order to be more detailed in every campsite in the case of each month, we calculate the median and quartile of time which visitors spend on campsite and time of visitors enter the preserve from May to September. As is shown in figure 2-3, every row represents a campsite, every column represents a month, 'E' in Y-axis represents time of entry preserve, its value corresponds to the upper scale, 'K' in Y-axis represents time of visitor spend in the campsite, its value corresponds to the scale below. This figure indicates that most visitor enter the preserve after 8 o'clock, however, enter time is no later than 16 o'clock. Meanwhile, this figure reflects the relation between time stay in campsite and month, for instance, we explore that visitors tended to arrange one day trip to campsite5, campsite4, campsite3, campsite2 or campsite0, if they wanted to enjoy a multiple day (2-4 days) trip, they would choose campsite8, campsite7, campsite6 or campsite1. The patterns of long and short travel of each month are different, maybe there were some special events or regular care in each campsite. Figure 2-3: Time spent in campsite and time of entry preserve in different campsite and month
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.
We analyzed the low frequency path of vehicles without camping and found: Figure 3-1: Duction of outliers There are six cars enter entrance No.11 in order from 10:28 pm to 10:30 pm, and soon entered entrance No.41 (ranger's work area) and stayed for 2-4 hours. and then across entrance No.41, and finally leave the park from entrance No.11. It is very questionable to gang enter ranger's work area. Figure 3-2: Duction of outliers There are 23 trucks entering the park from entrance No.13 at two o'clock in the morning. There is frequent acrossing pass such as entrance No.36, No.46, No.35, No.33, No.43 and No.46, and then they gang enter work area of ranger and stay for one hour. The behavior is also very questionable. Figure 3-3: Duction of outliers Ifile:///Users/Sandra/CNIC/VAST/VAST%20Challenge1/VAST%20Challenge%202017%20MC1%20Answer%20Sheet/VAST%20Challenge%202017%20MC1%20Answer%20Sheet_files/q3-4.pngf a car or a large truck through a park entrance into the park in the evening, and put the car stay here a whole night, go away in the morning or noon in the second day, then we could suspect that these people are get off to do something illegal. Using the trajectory data, we calculate the total count of passing campsite movement records and the duration time of each user. As is shown in Figure, and we identify three types of outliers. Figure 3-4:Scatter plot of movement count and total duration time Figure 3-5:movement pattern of identified outliers Figure 3-5 part A shows the movement pattern of individuals whose move counts are three, which indicated that they arrived at campsites without passing general gates and such behaviors were abnormal compared to most of the records. In this figure, the angle axis represents date, the radius axis represents the 24 hours in one day, each line is a vehicle trajectory through the park. The duration time of these trajectories in this figure is about an hour which is obviously less than other records and sparse in different dates. Figure 3-5 part B shows the movement pattern of individuals whose move counts are more than 20, its angle axis represents time, each line also is a trajectory. Trajectories numbers of this pattern are very little, staying in the park for a few days, and they went to more than two campsites different from most of the behaviors only went to a campsite could be found in the figure. Figure 3-5 part C shows the pattern of individuals who stay in the park for too long, its axis is same as B. It seems untrustworthy to a visitor stay in the park for a month.
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)
1. Illegal access to ranger work area. 2. Vehicles crossing the park at night, causing too much noise, light and pollution. 3. Overnight camping, causing too much noise, light and pollution.