# Indoor Location Competition 2.0 (Sample Data and Code)
This repository contains sample data and code for [Indoor Location Competition 2.0](https://aka.ms/location20), a continuation of Microsoft Indoor Location Competition. Competition this year will be completely virtual and evaluated on large-scale real indoor location datasets. The dataset to be released consists of dense indoor signatures of WiFi, geomagnetic field, iBeacons etc., as well as ground truth collected from hundreds of buildings in Chinese cities.
## Webinar Video
We held a webinar in July, the video is [here](https://www.youtube.com/watch?v=xt3OzMC-XMU).
## Sample Data
`data` folder contains indoor traces from two sites. Each trace (`*.txt`) corresponds to an indoor path between position p<sub>1</sub> and p<sub>2</sub> walked by a site-surveyor. During the walk, site-surveyor is holding an Android smartphone flat in front of his body, and a sensor data recording app is running on the device to collect IMU (accelerometer, gyroscope) and geomagnetic field (magnetometer) readings, as well as WiFi and Bluetooth iBeacon scanning results. A detailed description of the format of trace file is shown below. In addition to raw traces, floor plan metadata (e.g., raster image, size, GeoJSON) are also included for each floor.
### Trace File Format（*.txt）
| Time | Data Type | Value | | | | | | | |
|----------------------|-----------------------------------------------------|------------------------------------------|-------------------|--------------|------------------|---------------------|-------------------|-------------------|----------------------------------------|
| 1574659531598 | TYPE\_WAYPOINT | 196\.41757 | 117\.84907 | | | | | | |
| | Location surveyor labeled on the map | Coordinate x (meter) | Coordiante y (meter) | | | | | | |
| | | | | | | | | | |
| 1574659531695 | TYPE\_ACCELEROMETER | \-1\.7085724 | \-0\.274765 | 16\.657166 | 2 | | | | |
| | Android Sensor\.TYPE\_ACCELEROMETER | X axis | Y axis | Z axis | accuracy | | | | |
| 1574659531695 | TYPE\_GYROSCOPE | \-0\.3021698 | 0\.2773285 | 0\.107543945 | 3 | | | | |
| | Android Sensor\.TYPE\_GYROSCOPE | X axis | Y axis | Z axis | accuracy | | | | |
| 1574659531695 | TYPE\_MAGNETIC\_FIELD | 20\.181274 | 16\.209412 | \-32\.22046 | 3 | | | | |
| | Android Sensor\.TYPE\_MAGNETIC\_FIELD | X axis | Y axis | Z axis | accuracy | | | | |
| 1574659531695 | TYPE\_ROTATION\_VECTOR | \-0\.00855688 | 0\.051367603 | 0\.362504 | 3 | | | | |
| | Android Sensor\.TYPE\_ROTATION\_VECTOR | X axis | Y axis | Z axis | accuracy | | | | |
| | | | | | | | | | |
| 1574659531695 | TYPE\_ACCELEROMETER\_UNCALIBRATED | \-1\.7085724 | \-0\.274765 | 16\.657166 | 0\.0 | 0\.0 | 0\.0 | 3 | |
| | Android Sensor\.TYPE\_ACCELEROMETER\_UNCALIBRATED | X axis | Y axis | Z axis | X axis | Y axis | Z axis | accuracy | |
| 1574659531695 | TYPE\_GYROSCOPE\_UNCALIBRATED | \-0\.42333984 | 0\.20202637 | 0\.09623718 | \-7\.9345703E\-4 | 3\.2043457E\-4 | 4\.119873E\-4 | 3 | |
| | Android Sensor\.TYPE\_GYROSCOPE\_UNCALIBRATED | X axis | Y axis | Z axis | X axis | Y axis | Z axis | accuracy | |
| 1574659531695 | TYPE\_MAGNETIC\_FIELD\_UNCALIBRATED | \-29\.830933 | \-26\.36261 | \-300\.3006 | \-50\.012207 | \-42\.57202 | \-268\.08014 | 3 | |
| | Android Sensor\.TYPE\_MAGNETIC\_FIELD\_UNCALIBRATED | X axis | Y axis | Z axis | X axis | Y axis | Z axis | accuracy | |
| | | | | | | | | | |
| 1574659533190 | TYPE\_WIFI | intime\_free | 0e:74:9c:a7:b2:e4 | \-43 | 5805 | 1574659532305 | | | |
| | Wi\-Fi data | ssid | bssid | RSSI | frequency | last seen timestamp | | | |
| | | | | | | | | | |
| 1574659532751
快速上手Ai理论及应用实战：基础知识、ML、DL、NLP-BERT.zip （893个子文件） 
train_waypoints.csv 7.69MB hangout_chat_data.csv 53KB world-happiness-report-2017.csv 29KB auto_data.csv 25KB .DS_Store 6KB us_job_market_discrimination.dta 1.04MB
202012310000.gif 370KB 2021010109030000.gif 58KB .gitignore 39B 第二章——手写线性回归算法.iml 623B NLP处理实例.ipynb 4.34MB 3_模型解释与分析_建筑能源利用率预测.ipynb 3.73MB 3_模型解释与分析_建筑能源利用率预测-checkpoint.ipynb 3.73MB 疾病引起原因.ipynb 3.27MB 疾病引起原因-checkpoint.ipynb 3.27MB 快手用户活跃预测.ipynb 1.89MB 快手用户活跃预测-checkpoint.ipynb 1.89MB data_abstract_sensor.ipynb 1.89MB 特征工程建模-银行违约预测.ipynb 1.33MB post_process.ipynb 1.25MB post_process-checkpoint.ipynb 1.25MB 案例：汽车价格预测任务-checkpoint.ipynb 1.24MB 案例：汽车价格预测任务.ipynb 1.24MB 1_数据预处理_建筑能源利用率预测.ipynb 1.22MB 1_数据预处理_建筑能源利用率预测-checkpoint.ipynb 1.22MB 模型解释-工具介绍及使用.ipynb 596KB 模型解释-工具介绍及使用-checkpoint.ipynb 596KB 图像特征.ipynb 401KB 图像特征-checkpoint.ipynb 401KB 贝叶斯建模-checkpoint.ipynb 379KB 贝叶斯建模.ipynb 379KB Optuna一种超参数优化框架.ipynb 331KB Optuna一种超参数优化框架-checkpoint.ipynb 331KB 2_建模_建筑能源利用率预测.ipynb 293KB 2_建模_建筑能源利用率预测-checkpoint.ipynb 293KB Automated feature engineering.ipynb 292KB Automated feature engineering-checkpoint.ipynb 292KB 1-道路通行时间预测.ipynb 284KB 1-道路通行时间预测-checkpoint.ipynb 284KB 聚类案例实战.ipynb 224KB 逻辑回归-信用卡欺诈检测-checkpoint.ipynb 195KB 逻辑回归-信用卡欺诈检测.ipynb 195KB lstm-wifi-encode-wifi-sensor.ipynb 178KB lstm-wifi-encode-wifi-sensor-checkpoint.ipynb 178KB 酒店推荐.ipynb 159KB 酒店推荐-checkpoint.ipynb 159KB 工业生产预测.ipynb 134KB 工业生产预测-checkpoint.ipynb 134KB lstm-wifi-encode-wifi.ipynb 131KB lstm-wifi-encode-wifi-checkpoint.ipynb 131KB 常用特征构造方法.ipynb 104KB 常用特征构造方法-checkpoint.ipynb 104KB Python层次聚类.ipynb 87KB Python层次聚类-checkpoint.ipynb 87KB create-unified-wifi-features-example.ipynb 77KB create-unified-wifi-features-example-checkpoint.ipynb 77KB Untitled-checkpoint.ipynb 72KB python假设检验实例.ipynb 64KB python假设检验实例-checkpoint.ipynb 64KB LSTM情感分析-checkpoint.ipynb 52KB LSTM情感分析.ipynb 52KB rules_infer.ipynb 49KB 文本特征处理.ipynb 48KB 文本特征处理-checkpoint.ipynb 48KB 相关分析.ipynb 45KB 相关分析-checkpoint.ipynb 45KB 2-建模预测.ipynb 40KB 2-建模预测-checkpoint.ipynb 40KB 高阶与分类变量实例-checkpoint.ipynb 38KB 高阶与分类变量实例.ipynb 38KB floor-model-blstm.ipynb 33KB floor-model-blstm-checkpoint.ipynb 33KB 99-80-floor-accurate-model-blstm-checkpoint.ipynb 33KB Stacking.ipynb 32KB Stacking-checkpoint.ipynb 32KB wifi-label-encode.ipynb 31KB 假设检验.ipynb 31KB 假设检验-checkpoint.ipynb 31KB Feature Engineering Techniques.ipynb 30KB Feature Engineering Techniques-checkpoint.ipynb 30KB statsmodels回归分析-checkpoint.ipynb 29KB statsmodels回归分析.ipynb 29KB data_abstract_wifi.ipynb 27KB 方差分析-checkpoint.ipynb 19KB 方差分析.ipynb 19KB 特征工程建模-银行违约预测-checkpoint.ipynb 16KB python卡方检验实例.ipynb 14KB wifi-features.ipynb 13KB wifi-features-checkpoint.ipynb 13KB testpath2gap.ipynb 12KB gen_accl.ipynb 6KB 层次聚类.ipynb 6KB 层次聚类-checkpoint.ipynb 6KB NLP处理实例-checkpoint.ipynb 5KB Python方差分析实例.ipynb 5KB combine_v1.ipynb 2KB combine_v1-checkpoint.ipynb 2KB combine_v2.ipynb 2KB combine_v2-checkpoint.ipynb 2KB python卡方检验实例-checkpoint.ipynb 72B共 893 条
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