spatial-autocorrelation

There are 18 repositories under spatial-autocorrelation topic.

  • r-spatial/spdep

    Spatial Dependence: Weighting Schemes and Statistics

    Language:R1251511127

  • ghislainv/forestatrisk

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

    Language:Python11973827
  • forestatrisk-tropics

    ghislainv/forestatrisk-tropics

    :earth_africa: :pencil: Modelling and forecasting deforestation in the tropics

    Language:R29402

  • oliviergimenez/spatial-stream-network-occupancy-model

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

    Language:TeX410

  • viktormiok/Csppa

    Machine learning analysis & visualisation of cellular spatial point patterns

    Language:R3201

  • atalbanese/NEON_Hyperspectral

    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.

    Language:Python2100

  • viktormiok/CsppaRshiny

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

    Language:R2301

  • cjabradshaw/SavannaCorridors

    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

    Language:R1302

  • lophital/residualsofsdm

    :smirk:       

    1100

  • MartaFatto/Geospatial-data-science-Airbnb

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

    Language:Jupyter Notebook1100

  • GretaGalliani/public_transport_COVID_Lombardy

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

    Language:R0100

  • mkupisie/Calculating-spatial-autocorrelation-of-income-pySAL-esda-geopandas

    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.

    Language:Jupyter Notebook0100

  • Wafama/Thesis_Code

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

    Language:Jupyter Notebook0101

  • cjabradshaw/childDiarr

    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.

    Language:R102

  • EveSeward/Tutorial-for-Spatial-Autocorrelation-Analysis-in-R

    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.

  • Geo3D-AI-CSU/GCN-LSTM

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

    Language:Python

  • nanotubing/statistics

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

    Language:R10

  • omarkawach/spatial_analysis_scenarios

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

    Language:Jupyter Notebook301