Spatial Statistical analyses created using R and RStudio for an "Advanced Statistics for Urban Applications" at Temple University

The goal is to develop a method that automates the generation of large-scale, spatial DEVS simulation models from GIS data

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Geospatial data analysis, street network analysis, spatial autocorrelation, maps

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".

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

🌍 📝 Modelling and forecasting deforestation in the tropics

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.

Classifying Travel Mode choice in the Netherlands using KNN, XGBoost, RF and TabNet

📦🐍 Python package to model and forecast the risk of deforestation

Machine learning analysis & visualisation of cellular spatial point patterns

Data, code and manuscript for 'Spatial occupancy models for data collected on stream networks'

The R Shiny App for machine learning analysis and visualization of cellular spatial point patterns under hypercaloric diet shifts.

Spatial Dependence: Weighting Schemes and Statistics

This tutorial uses Global Moran’s I and Local Interpretation of Spatial Autocorrelation (LISA) testing methods to determine the spatial correlation between median total income and the percentage of French knowledge speakers in Kelowna, British Columbia.

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.