spatial-autocorrelation

Spatial Dependence: Weighting Schemes and Statistics

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

🌍 📝 Modelling and forecasting deforestation in the tropics

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

Ununennium (Element 119, the next alkali metal) represents the cutting edge of satellite imagery machine learning. This library provides a unified, GPU-first framework for end-to-end Earth observation workflows, from cloud-native data access through model training to deployment.

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.

Machine learning analysis & visualisation of cellular spatial point patterns

A groundwater level spatiotemporal prediction model based on graph convolutional networks with a long short-term memory

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

🔍 Explore and analyze ununennium, a Python package designed for efficient data manipulation and visualization. Streamline your data workflows today.

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.

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

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

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

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.

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

Global and local indicators of spatial autocorrelation (Moran's I, LISA) in residency by race across Harris County, Texas

Code developed for the paper "The Impact of Public Transport on the Diffusion of the COVID-19 Pandemic in Lombardy during 2020".

A geospatial data analytics project analyzing 26 years of UK economic (GVA) and deprivation (IMD) data. Features an ETL pipeline, spatial statistics, and interactive visualizations.