COVID-19-Community-Mobility-Dataset

Name: COVID-19-Community-Mobility-Dataset
Creator: deepstartup

覆盖120多个国家的社区流动性数据，用于分析COVID-19感染率影响。

deepstartup

GitHub

2020-04-27 更新

表格COVID-19社区流动性

基本信息

模态

表格

创建/更新时间

2020-04-27

资源简介

该数据集源自谷歌COVID-19社区流动性报告，覆盖120多个国家，包含各国ISO2代码、国家名称、日期及日常活动场所的流动性趋势等详细信息，用于分析社区流动性对COVID-19感染率的影响。

原始链接

https://github.com/deepstartup/COVID-19-Community-Mobility-Dataset

访问原始数据

官方服务

如需原始数据获取支持或标注服务，请联系我们。

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下载信息

注册下载

Tips: 该数据集需要在对应的数据源网站注册通过后，才能进行数据下载，注册有对应要求，或者需要收费。

暂未开放

公开下载

Tips: 该数据集属于公开下载，应该可以免费公开下载。

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有偿下载

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提供高速下载与技术交付服务（收技术服务费，非数据销售）

暂未开放

千方医数集，医疗数据集部分，是为社区服务的公开医疗数据集搜索引擎，并不存储或者下载原始的任何数据。如果您有其他医疗数据需求，可以和客服联系，或者下工单。我们有强大的三甲医疗机构帮助您提供个性化的医疗数据定制、采集、标注服务。

使用方式

数据集获取

git clone https://github.com/deepstartup/COVID-19-Community-Mobility-Dataset.git

curl -L -o repo.zip https://github.com/deepstartup/COVID-19-Community-Mobility-Dataset/archive/refs/heads/master.zip
unzip repo.zip

源站 README 摘录（使用方式）

COVID-19-Community-Mobility-Dataset

The Dataset has been extracted from google https://www.google.com/covid19/mobility/ for 120+ countries and have been tried to look into deep inside the mobility data. This data may help to understand community mobility inpacts on infection rate both countrywise as well as worldwide.
File:

community_dataset_with_infection_count.csv
____________________________________________
# Description of fields :
1.COUNTRY_REGION_CODE->ISO2 country code
2.COUNTRY_REGION->Country Name
3.DATE_VAL->Date
4.DAY_CT-> Day number starting from 15-Feb-2020
5.RETAIL_AND_RECREATION_PCT->Mobility trends for places like restaurants,
cafes, shopping centers, theme parks,
museums, libraries, and movie theaters.
6.INC_RETAIL_AND_RECREATION_PCT-Increase Mobility trends for places like restaurants,
cafes, shopping centers, theme parks,
museums, libraries, and movie theaters.
7.GROCERY_AND_PHARMACY_PCT->Mobility trends for places like grocery
markets, food warehouses, farmers
markets, specialty food shops, drug stores,
and pharmacies.
8.INC_GROCERY_AND_PHARMACY_PCT-> Increase Mobility trends for places like grocery
markets, food warehouses, farmers
markets, specialty food shops, drug stores,
and pharmacies.
9.PARKS_PCT->Mobility trends for places like national parks,
public beaches, marinas, dog parks, plazas,
and public gardens.
10.INC_PARKS_PCT-> Increase Mobility trends for places like national parks,
public beaches, marinas, dog parks, plazas,
and public gardens.
11.TRANSIT_STATIONS_PCT-> Mobility trends for places like public transport
hubs such as subway, bus, and train stations.
12.INC_TRANSIT_STATIONS_PCT-> Increase  Mobility trends for places like public transport
hubs such as subway, bus, and train stations.
13.WORKPLACES_PCT-> Mobility trends for places of work
14.INC_WORKPLACES_PCT -> Increase Mobility trends for places of work
15.RESIDENTIAL_PCT -> Mobility trends for places of residence 
16.INC_RESIDENTIAL_PCT -> Increase Mobility trends for places of residence
17.COVID_CONFIRMED -> Confirm Covid cases for that country.
18.COVID_RECOVERED -> Confirm Recover cases for that country.
19.COVID_DEATHS -> Confirm Death  cases for that country.
20.COVID_CNTY_NEW_CASES -> New Covid cases for that country.
21.COVID_COUNTRY_INC_RATE -> Increase New Covid cases for that country.
22.COVID_WORLD_CONFIRMED > Confirm Covid cases for that World.


## 数据加载示例（表格/文本类）

```python
import pandas as pd, glob, os

files = (glob.glob(os.path.join(path, "**", "*.csv"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tsv"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.xlsx"), recursive=True))
print("数据文件:", files)
df = pd.read_csv(files[0])
print(df.shape); print(df.columns.tolist()); print(df.head(3))
```



> 完整仓库：[github.com/deepstartup/COVID-19-Community-Mobility-Dataset](https://github.com/deepstartup/COVID-19-Community-Mobility-Dataset)