Spatial Statistical analyses created using R and RStudio for an "Advanced Statistics for Urban Applications" at Temple University
-
Updated
Mar 18, 2019 - R
Spatial Statistical analyses created using R and RStudio for an "Advanced Statistics for Urban Applications" at Temple University
πΒ Β Β Β Β Β Β
Data, code and manuscript for 'Spatial occupancy models for data collected on stream networks'
Scripts to create tree species classification models from NEON Science hyperspectral and vegetation data. Created as part of my master's thesis in GeoInformatics at Hunter College, 2023.
Code developed for the paper "The Impact of Public Transport on the Diffusion of the COVID-19 Pandemic in Lombardy during 2020", authored by Greta Galliani, Piercesare Secchi, and Francesca Ieva, published in Medical Research Archives in September 2023.
Calculating global and local spatial autocorrelation of income noted per each polish county in 2022 based on Moran's I and LISA statistics. Calculations were conducted using the following packages: pySAL, splot.esda, geopandas.
Geospatial data analysis, street network analysis, spatial autocorrelation, maps
Classifying Travel Mode choice in the Netherlands using KNN, XGBoost, RF and TabNet
The goal is to develop a method that automates the generation of large-scale, spatial DEVS simulation models from GIS data
The R Shiny App for machine learning analysis and visualization of cellular spatial point patterns under hypercaloric diet shifts.
Machine learning analysis & visualisation of cellular spatial point patterns
Determining the most important predictors of diarrhoea in children under five in South and Southeast Asia by exploring the spatiotemporal association between diarrhoeal incidence and various behavioural, socio-demographic, and environmental factors.
Analysis of palaeoecological records across South-East Asia to determine the evidence for regime shifts between open savannas and dense tropical forests occurred since the Last Glacial Maximum
π π Modelling and forecasting deforestation in the tropics
Spatial Dependence: Weighting Schemes and Statistics
π¦π Python package to model and forecast the risk of deforestation
Add a description, image, and links to the spatial-autocorrelation topic page so that developers can more easily learn about it.
To associate your repository with the spatial-autocorrelation topic, visit your repo's landing page and select "manage topics."