Kicking off the #30DayMapChallenge by visualizing the network formed by 8,697 railway stations in India.

Day 1: Points

Looking forward to explore India through geospatial visualizations with this challenge.

Made with @geopandas @matplotlib

A day in the Indian Railways Network. Animating Rajdhani trains moving from and to New Delhi.

#30DayMapChallenge Day 02: Lines

Made with #Python @matplotlib @geopandas

Data @DataPortalIndia @datameet #gischat #dataviz

Literacy in India - understanding the picture at district level from census data. Just over a quarter districts have literacy above 70%.

#30DayMapChallenge Day 03: Polygons
Made with @matplotlib @geopandas data: @censusindia2021

#dataviz #census

Access to healthcare - visualizing concentration of hospitals across India.

#30DayMapChallenge Day 04: Hexagons
made with @matplotlib data @nhpindia
#dataviz #healthcare #python

Elevation of Rivers: #30DayMapChallenge Day 05: Blue

Attempt to understand changing heights of Indian Rivers from @openstreetmap and DEM data.

Made with @matplotlib #rasterio #openstreetmap #python

Land Surface Temperature: #30DayMapChallenge Day 06: Red

Remarkable changes in land surface temperatures across India in 2020.

Made with @matplotlib #rasterio #python @NASAEarthData

Urban Green Spaces in India: #30DayMapChallenge Day 07: Green

How green are Indian cities? Looking at spaces including parks, forests, recreation grounds, etc. within 25km grids.

Made with @matplotlib @geopandas
Data from @openstreetmap
#python #openstreetmap #dataviz

Solar Potential in India: #30DayMapChallenge Day 08: Yellow

Visualizing direct normal solar irradiance throughout the year with current installed capacity of solar plants.

Made with @matplotlib @geopandas
Data from @NREL @WorldResources @mnreindia
#python #solar #renewable

Air Quality in India: Comparing AQI and PM2.5 particulate matter across major Indian cities.

As on today, #DelhiAirQuality is 4x that of any other major Indian city.

#30DayMapChallenge Day 09: Monochrome
Made with @matplotlib data @AQI_India #AirQuality

Plant Growth Patterns: Changing green vegetation in India

#30DayMapChallenge Day 10: Grid
Made with @matplotlib with #NDVI data from @NASAEarthData
#python #gischat

*^ sorry, supposed to be Western Ghats.

Hills of southern India: Visualizing exaggerated terrain model over @OpenStreetMap map

#30DayMapChallenge Day 11: 3D
Made with #rayshader #rstats, using @gebcobathymetry elevation data, @Esri basemap
#rspatial  #gistribe @osm_in

Can you create editable #Maps in @PowerPoint?

Visualizing rainfall patterns in India with PowerPoint. Slides and objects are auto-generated from data.

#30DayMapChallenge Day 12: Map not made with GIS software
Made with @Gramener SlideSense , data @Indiametdept #gischat #GIS

30 Years of Ganga Koshi Confluence: Tracing changing river courses from Landsat imagery.
Uses band combination of SWIR1,NIR,Red for color.

#30DayMapChallenge Day 13: Raster
Made with #geemap (thanks to @giswqs) #ipyleaflet data from @nasa_landsat #python #EarthEngine

Rainfall Anomaly in India: Tracking unpredictable rainfall changes. We compare annual rainfall deficits since 2001 with average baseline of 1980-2000.

#30DayMapChallenge Day 14: Climate change
made with @matplotlib data @Indiametdept #python #gis #climatechange

The Toy Train Connection: Built in the 1880s, this mountain railway spanning 88km connects New Jalpaiguri (100m) to Darjeeling (2,200m elevation)

#30DayMapChallenge Day 15: Connections
Using #rstats #rayshader @isro cartodem, #openstreetmap #rspatial @osm_in @tylermorganwall

Islands of Power held by Women: A look at constituencies that have elected at least one woman MP since 1962.

There are still ~50% constituencies yet to elect a woman MP to the Indian Parliament.

#30DayMapChallenge Day 16: Islands
Made with @matplotlib #python @ecisveep

18th Century Kingdoms of India: Interesting to see how the terrain separates the kingdoms of Malabar and Carnatic from Mysore empire, and Northern Sarkars and Nizams from the Marathas.

#30DayMapChallenge Day 17: Historical map from 1923 Atlas. Made #rstats #rayshader #rspatial

India's Urban Sprawl: Understanding how Indian cities have grown from 1985 to 1995 and to 2005, using land cover and land use data.

#30DayMapChallenge Day 18: Landuse
Made with @Matplotlib Data: @NASAEarthData DAAC ORNL #python #dataviz #rasterio

Areas without Train Stations: These are 30x30 km grid areas in India without a train station.

#30DayMapChallenge Day 19: NULL
Data: #openstreetmap Made: @Matplotlib #python #gis

Population density of India: Pretty conspicuous that northern India across the fertile plains of river Ganges is much more densely packed than the rest of India.

#30DayMapChallenge Day 20: Population
Made with #rstats #rayshader Data from GHSL 2015 estimate. #rspatial #QGIS

Dams of Central India: A look at how dams are built around some of the major rivers of India.

#30DayMapChallenge Day 21: Water
Made with @Matplotlib Data: GRanD, #OpenStreetMap
#python #dataviz

Migration in India: Mapping Inter-State migratory movements

Interesting to see that apart from Delhi and Mumbai, most state migrations happen just around the state borders.

#30DayMapChallenge Day 22: Movement
Made with @matplotlib data census 2011
#migration #python #dataviz

Nearest Capital-based Boundaries: Mapping each district of India to the nearest capital city.

Distance from the nearest capital city is measured from district's center location.

#30DayMapChallenge Day 23: Boundaries
Made with @matplotlib #python #dataviz

The origins of Ganga: Alaknanda and Bhagirathi rivers meet at Devprayag (at 830m elevation) to form Ganga, and in less than 75kms, #Ganga drops to 370m at Rishikesh.

#30DayMapChallenge Day 24: Elevation
Using #rstats #rayshader @isro cartodem
#rspatial @osm_in #OpenStreetMap