8.22 Events / meet other spatial data professionals.8.19 Remote sensing background (optional).8.12 Urban area and temperature relationship.8.11 Calucating urban area from Landsat data.8.10.1 Calucating tempearture from Landsat data.8.5.1 Remote sensing background (required).7.7 Task 3 - Spatial Non-stationarity and Geographically Weighted Regression Models (GWR).7.6.6 TASK: Investigating Further - Adding More Explanatory Variables into a multiple regression model.7.6.5 Extending your regression model - Dummy Variables.7.6.1 Dealing with Spatially Autocorrelated Residuals - Spatial Lag and Spatial Error models.7.5.10 Assumption 5 - Independence of Errors.7.5.8 Assumption 3 - No Multicolinearity in the independent variables.7.5.7 Assumption 2 - The residuals in your model should be normally distributed.7.5.6 Assumption 1 - There is a linear relationship between the dependent and independent variables.7.5.5 Assumptions Underpinning Linear Regression.7.5 Analysing GCSE exam performance - testing a research hypothesis.6.9 Analysing Spatial Autocorrelation with Moran’s I, LISA and friends.6.7 Density-based spatial clustering of applications with noise: DBSCAN.4.5.6 If have have an existing project - way 3.4.5.5 Create a new version control in RStudio - way 2.1.4 The Basics of Geographic Information.How to download data and files from GitHub.
