数据分析报告代写 Changes on the scale of dataset

User Guide

Version 1.3 (2012/08/01)

数据分析报告代写 This Microsoft Research License Agreement, including all exhibits ("MSR-LA") is a legal agreement between you and Microsoft

Data Description 数据分析报告代写

This GPS trajectory dataset was collected in (Microsoft Research Asia) Geolife project 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.

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 1 plots 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

A）Data overview in Beijing                                            B) Within the 5^th Ring Road of Beijing

Figure 1 Distribution of the dataset in Beijing city

The distributions of distance and duration of the trajectories are presented in Figure 2 and Figure 3.

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 in Figure 5.

2.What’s new?  数据分析报告代写

• 
  
  
  
  • Transportation mode labels
  
  
  
  

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 section 4.2 for the format of labels.

The total distance and duration of transportation modes are listed in Figure 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.

• Changes on the scale of dataset

Changes on the scale of dataset between version 1.2 and version 1.3 are listed in Figure 7. Effective days refer to the total number of days where there’s a record in the dataset.

3.Paper Citation  数据分析报告代写

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.  数据分析报告代写

• 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.


• 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.Data Format

• 
  
  
  
  • Trajectory file
  
  
  
  

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.

PLT format:  数据分析报告代写

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.

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.

Example:  数据分析报告代写

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

• 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.

Example:

Start Time              End TimeTransportation Mode

2008/04/02 11:24:21            2008/04/02 11:50:45            bus

2008/04/03 01:07:03            2008/04/03 11:31:55            train

2008/04/03 11:32:24            2008/04/03 11:46:14            walk

2008/04/03 11:47:14            2008/04/03 11:55:07            car  数据分析报告代写

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 the 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. 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.

5.Contact  数据分析报告代写

If you have any questions about this dataset, please contact Dr. Yu Zheng from Microsoft Research Asia.

Yu Zheng

Tel: 86-10-59173038 Email: yuzheng@microsoft.com

Homepage: http://research.microsoft.com/en-us/people/yuzheng/default.aspx

Address: Microsoft Research Asia, Tower 2, No. 5 Danling Street, Haidian District, Beijing, P.R. China 100080

6.Release history

Version 1.3 (released 2012/08/01) Version 1.2 (released 2011/10/31) Version 1.1 (released 2011/07/25) Version 1.0 (released 2010/09/30)

7.Microsoft Research License Agreement

Non-Commercial Use Only

<< GeoLife GPS Trajectories>>  数据分析报告代写

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