Name: Rajeswari Rama Kamala Parasa
Type: User
Company: University of South Brittany, University of Salzburg
Bio: Master's student at Copernicus Digital Earth. Presently dabbling around with spatial data and urban science.
Twitter: Rajesvari7
Location: Vannes, France
Blog: https://rajesvariparasa.github.io/
Rajeswari Rama Kamala Parasa's Projects
Exploratory data analysis, flood prediction model implementation with the DFC 2024 Challenge Track 1 dataset
Official GeoTools repository
Delhi Development Authority recently released draft land use plan 2041 for Delhi and called for public comments. This repo has some of the geospatial layers that I was able to extract from the PDF map of the draft plan.
A location allocation model I tried based on euclidean or radial distance. Concept used: Voronoi Tessellation
Appendix to the Working Paper "Methods to measure spatial access to healthcare facilities in cities"
This repository contains an ML workflow to predict house prices in Ames, Iowa. This project work is carried out under the Machine Learning module of the GeoDSc track of the Copernicus Master in Digital Earth.
Spatial Data of Municipalities
NFHS-5: National Family Health Survey (2019-20). CSV fact sheets (states, districts) for key indicators from http://rchiips.org/nfhs/
This is a public repo
More to come soon
PLUS_softwaredev_2023_some-unique-tag
Test site for visualising Pune POIs data
A library to create, solve, and analyze spatial optimization problems
This Jupyter Notebook has been developed based on "A Beginner's Python Tutorial" by Steven Thurlow. The original material has been ported to Python 3.
A prototype script to test if OSMnx library can be used to extract spatial railway routes, based on railway schedule data, from OpenStreetMaps' roads data by taking CSV files comprising the station locations along the route as input.
Find out more about me here! This personal website was built using a Jekyll theme - minimal-mistakes.
Using OSMnx, OSRM, and Google Maps Directions API with Python to calculate shortest, fastest, and traffic-based most-efficient routes for a set of origin and destination points
This notebook demonstrates timeseries classification for crop identification on a subset of the MiniTimeMatch dataset by training an LSTM model.
Characterizing urban land use with machine learning