flowmaps-data

A tool for downloading COVID-19 and mobility datasets for Spain.

The database integrates two main types of data:

1. Time dependent population mobility networks across Spain (provided by MITMA and INE)

2. Daily reports of COVID-19 cases in Spain, at different levels of spatial resolution (provided by the CNE and the different Autonomous Communities)

All the data records are associated with a specific area from a geographic layer:

3. Geographic layers for Spain, in geojson format, at different levels of spatial resolution.

All the data has been gather from official access points.

More info about the data: https://flowmaps.life.bsc.es/flowboard/data

API: https://flowmaps.life.bsc.es/api

Contact us: https://flowmaps.life.bsc.es/flowboard/contact

Installation

Install using pip:

pip install flowmaps-data

Install manually:

Create virtual environment:

virtualenv env --python=python3
source env/bin/activate

Install python dependencies:

pip3 install -r requirements.txt

Usage

Command line utility

usage: flowmaps-data [-h] COLLECTION [list describe download]

examples: 

    # Geojson layers
    flowmaps-data layers list
    flowmaps-data layers describe --layer cnig_provincias --provenance
    flowmaps-data layers describe --layer cnig_provincias --plot
    flowmaps-data layers download --layer cnig_provincias

    # Consolidated COVID-19 data
    flowmaps-data covid19 list
    flowmaps-data covid19 describe --ev ES.covid_cpro
    flowmaps-data covid19 download --ev ES.covid_cpro --output-file out.csv --output-type csv

    # Deceased datasets
    flowmaps-data deceased list
    flowmaps-data deceased describe --ev ES.hosp_covid_cpro
    flowmaps-data deceased download --ev ES.hosp_covid_cpro --output-file out.csv --output-type csv

    # Population
    flowmaps-data population list
    flowmaps-data population describe --layer cnig_provincias
    flowmaps-data population download --layer zbs_15 --output-file out.csv

    # Origin-destination daily mobility (from MITMA)
    flowmaps-data daily_mobility_matrix list
    flowmaps-data daily_mobility_matrix describe
    flowmaps-data daily_mobility_matrix download --source-layer cnig_provincias --target-layer cnig_provincias --start-date 2020-10-10 --end-date 2020-10-16 --output-file out.csv

    # Daily zone movements (from MITMA)
    flowmaps-data zone_movements list
    flowmaps-data zone_movements describe
    flowmaps-data zone_movements download --layer cnig_provincias --output-file out.csv --start-date 2020-10-10 --end-date 2020-10-10

    # Other datasets
    flowmaps-data datasets list
    flowmaps-data datasets describe --ev ES.covid_cpro
    flowmaps-data datasets download --ev ES.covid_cpro --output-file out.csv --output-type csv

    # Mobility Associated Risk
    flowmaps-data risk list
    flowmaps-data risk list-dates
    flowmaps-data risk download --source-layer cnig_provincias --target-layer cnig_provincias --ev ES.covid_cpro --date 2020-10-10 --output-file out.csv --output-format csv

Python module

from flowmaps_data import geolayer, covid19, dataset, daily_mobility_matrix, population, zone_movements

# Geojson layers
geojson = geolayer('cnig_provincias')

# Consolidated COVID-19 data
df = covid19(ev='ES.covid_cpro')

# Raw health datasets
df = dataset(ev='ES.covid_cpro')

# Origin-destination daily mobility (from MITMA)
df = daily_mobility_matrix(source_layer='cnig_provincias', target_layer='cnig_provincias', start_date='2020-11-01', end_date='2020-12-01', source='28', target='08')

# Daily zone movements (from MITMA)
df = zone_movements(layer='cnig_provincias')

# Population
df = population('cnig_provincias')

More examples

Plot daily incidence by province

from flowmaps_data import geolayer, covid19
import plotly.graph_objects as go

# Download geojson layer
geojson = geolayer('cnig_provincias')

# Download COVID-19 data
df = covid19(ev='ES.covid_cpro')

# Select data for one date
date = '2020-10-10'
df = df[df['date'] == date]

# Plot
fig = go.Figure(go.Choroplethmapbox(geojson=geojson,
                                    locations=date_df['id'],
                                    z=date_df['new_cases'],
                                    colorscale="Reds",
                                    marker_opacity=0.8))
fig.update_layout(title=f'Covid-19 daily incidence at {date}',
                  mapbox_style="carto-positron",
                  mapbox_zoom=4.5,
                  mapbox_center={"lat": 40.495178477814555, "lon": -3.717336960173357})
fig.update_layout(margin={"r":0,"t":30,"l":0,"b":0})
fig.show()