Geospatial Data Carpentry for Urbanism: All Images

Introduction to R and RStudioProject management in RStudio Introduction to R

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R project organization

Exploring Data Frames & Data frame Manipulation with dplyrExploring Data frames Data frame Manipulation with dplyr

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A data frame
Source: Data Carpentry R for Social Scientists

Introduction to visualisationIntroduction to Visualisation Writing data

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Introduction to Geospatial Concepts

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The shape of the Earth. Source: United Nations Statistics Division and International Cartographic Association (2012a).

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Geographical latitude and longitude. Source: van der Marel (2014).

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Map projection represented as flattening an orange peel. Source: Data Carpentry (2023).

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Cylindrical, conic, and azimuthal map projections. Source: Knippers (2009).

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Explore and plot by vector layer attributesQuery Vector Feature Metadata

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Mobility network in Delft using thicker lines than the previous example.

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Map of the mobility network in Delft with large-font and border around the legend.

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Handling Spatial Projections & CRS

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The plot above uses the default colours inside ggplot2 for raster objects. We can specify our own colours to make the plot look a little nicer. R has a built in set of colours for plotting terrain, which are built in to the terrain.colors() function. Since we have three bins, we want to create a 3-colour palette:

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The axis labels x and y are not necessary, so we can turn them off by passing element_blank() to the relevant part of the theme() function.

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Reproject Raster Data

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Our results are curious - neither the Digital Terrain Model (DTM_TUD_df) nor the DTM Hillshade (DTM_hill_TUD_df) plotted. Let’s try to plot the DTM on its own to make sure there are data there.

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Raster Calculations

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Source: National Ecological Observatory Network (NEON).

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Notice that the range of values for the output CHM starts right below 0 and ranges to almost 100 meters. Does this make sense for buildings and trees in Delft?