moran-i

Spatial Modelling for Data Scientists

Course: Introduction to Geographic Data Science

Script do meu projeto de conclusão de curso no MBA em Data Science e Analytics pela USP/Esalq.

The study employs Moran’s I statistic and Local Indicators of Spatial Association (LISA) to analyze spatial patterns and dependencies in housing prices across U.S. counties.

A basic tutorial on implementing Moran's I analysis in R

This project aims to analyse the availability and accessibility of green zones in the city of Valencia, Spain, using spatial analysis techniques.

Spatial Sampling

Spatial analysis of poverty risk and socioeconomic inequalities across Italian NUTS 2 regions using R, global and local spatial statistics, and spatial regression models.

Analyse spatiale des déserts médicaux et du niveau de vie en France, à partir des données DREES/INSEE — autocorrélation spatiale (Moran's I), modèle SEM et cartographie LISA

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

Analysis of spatial distribution of income in Italy

Lattice Data with R (City of Cape Town)

spatial modelling

Research code, preprocessed data, and robustness analyses for studying COVID-19 impacts on urban shopping mobility and socioeconomic inequality in New York City.

Reproducible spatial econometrics project on digital inclusive finance and regional low-carbon transition using Chinese provincial panel data.

This is repository of python codes for spatial economics

Reproducible analysis code and figure-generation scripts supporting the study of regional dairy productivity convergence and distribution dynamics in Azerbaijan (2000–2024).

Applied spatial statistics project in R including geostatistical interpolation, variogram modelling, kriging, spatial point pattern analysis and epidemiological spatial regression.

Create variograms, krige, and generate correlograms by utilizing gstat and spdep!

The dataset records the total number of road traffic accidents in each state for the given period, categorizing the accidents into fatal, severe, and minor incidents. This comprehensive dataset is valuable for analyzing trends and patterns in road safety across the country, helping to identify regions with higher accident incidences.