数据集作业代写 These trajectories were recorded by different GPS loggers and GPS- phones, and have a variety of sampling rates. 91.5 percent of the
This GPS trajectory dataset was collected in (Microsoft Research Asia)Geolifeproject by 182 users in a period of over five years (from April 2007 to August 2012). A GPS trajectory of this dataset is represented by a sequence of time-stamped points, each of which contains the information of latitude, longitude and altitude. This dataset contains 17,621 trajectories with a total distance of 1,292,951kilometers and a total duration of 50, 176 hours.
These trajectories were recorded by different GPS loggers and GPS- phones, and have a variety of sampling rates. 91.5 percent of the trajectories are logged in a dense representation, e.g. every 1~5 seconds or every 5~10 meters per point. 数据集作业代写
This dataset recoded a broad range of users’ outdoor movements, including not only life routines like go home and go to work but also some entertainments and sports activities, such as shopping, sightseeing, dining, hiking, and cycling. This trajectory dataset can be used in many research fields, such as mobility pattern mining, user activity recognition, location -based social networks, location privacy, and location recommendation.
Although this dataset is wildly distributed in over 30 cities of China and even in some cities located in the USA and Europe, the majority of the data was created in Beijing, China. Figure 1plots the distribution (heat map) of this dataset in Beijing. The figures standing on the right side of the heat bar denote the number of points generated in a location.
Figure 1 Distribution of the dataset in Beijing city
In the data collection program, a portion of users have carried a GPS logger for years, while some of the others only have a trajectory dataset of a few weeks. This distribution is presented in Figure 4, and the distribution of the number of trajectories collected by each user is shown inFigure 5.
2.1. Transportation mode labels 数据集作业代写
73 users have labeled their trajectories with transportation mode, such as driving, taking a bus, riding a bike and walking. There is a label file storing the transportation mode labels in each user’s folder. See section4.2for the format of labels.
The total distance and duration of transportation modes are listed inFigure 6. Though this only covers a part of the dataset used in the following papers,the scale of this released dataset can still support transportation mode learning.
2.2. Changes on the scale of dataset
Changes on the scale of dataset between version 1.2 and version 1.3 are listed inFigure 7. Effective days refer to the total number of days where there’s a record in the dataset.
Please cite the following papers when using this GPS dataset.
 Yu Zheng, Lizhu Zhang, Xing Xie, Wei-Ying Ma.Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of International conference on World Wild Web (WWW 2009), Madrid Spain. ACM Press: 791 -800.
 Yu Zheng, Quannan Li, Yukun Chen, Xing Xie, Wei-Ying Ma.Understanding Mobility Based on GPS Data. In Proceedings of ACM conference on Ubiquitous Computing (UbiComp 2008), Seoul, Korea. ACM Press: 312-321. 数据集作业代写
 Yu Zheng, Xing Xie, Wei-Ying Ma,GeoLife: A Collaborative Social Networking Service among User, location and trajectory. Invited paper, in IEEE Data Engineering Bulletin. 33, 2, 2010, pp. 32 -40.
4.1. Trajectory file 数据集作业代写
Every single folder of this dataset stores a user’s GPS log files, which were converted to PLT format. Each PLT file contains a single trajectory and is named by its starting time. To avoid potential confusion of time zone, we use GMT in the date/time property of each point, which is different from our previous release.
Line 1…6 are useless in this dataset, and can be ignored. Points are described in following lines, one for each line.
Field 1: Latitude in decimal degrees.
Field 2: Longitude in decimal degrees.
Field 3: All set to 0 for this dataset.
Field 4: Altitude in feet (-777 if not valid).
Field 5: Date - number of days (with fractional part) that have passed since 12/30/1899.
Field 6: Date as a string. 数据集作业代写
Field 7: Time as a string.
Note that field 5 and field 6&7 represent the same date/time in this dataset. You may use either of them.
39.906631,116.385564,0,492,40097.5864583333,2009- 10- 11,14:04:30
39.906554,116.385625,0,492,40097.5865162037,2009- 10- 11,14:04:35
4.2. Transportation mode labels
Possible transportation modes are: walk, bike, bus, car, subway, train, airplane, boat, run and motorcycle. Again, we have converted the date/time of all labels to GMT, even though most of them were created in China.
End TimeTransportation Mode 数据集作业代写
bus train walk car
First, you can regard the label of both taxi and car as driving although we set them with different labels for future usage. Second, a user could label th transportation mode of a light rail as train while others may use subway as the label. Actually, no trajectory can be recorded in an underground subway system since a GPS logger cannot receive any signal there. 数据集作业代写
In Beijing, the light rails and subway systems are seamlessly connected, e.g., line 13 (a light rail) is connected with line 10 and line 2, which are subway systems. Sometimes, a line (like line 5) is comprised of partial subways and partial light rails. So, users may have a variety of understanding in their transportation modes. You can differentiate the real train trajectories (connecting two cities) from the light rail trajectory (generating in a city) according to their distances. Or, just treat them the same.
If you have any questions about this dataset, please contact Dr. Yu Zheng from Microsoft Research Asia.
Yu Zheng 数据集作业代写
Tel: 86- 10-59173038 Email: firstname.lastname@example.org
Address: Microsoft Research Asia, Tower 2, No. 5 Danling Street, Haidian District, Beijing, P.R. China 100080
(released 2011/10/31) 数据集作业代写
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