11/10/2023 0 Comments Fun negative correlation examples![]() Graticule = st_transform(graticule, world_robinson) World_robinson = "+proj=robin +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs"Ĭountries = st_transform(countries, world_robinson) The two maps above were created using the following code: Visualizations were created in R using the code snippets provided below the graphics.Ĭountries = st_read("2022-world-data.geojson") Statistics largely downloaded from World Bank.Ī GeoJSON file of this data is available here. The examples in this tutorial are based on a collection of world country 2021 GDP per Capita (PPP) (World Bank 2022) 2015 MM BTU per Capita (World Bank 2022) Example Data For that, you need to use scatterĬharts and regression. Is, especially if the patterns are complex. It is not particularly useful for rigorously determining how strong that relationship ![]() ![]() While visual comparison of maps is helpful for detecting correlation, That is evident by the strong similarity between these two maps: Travel, purchase material goods, or heat/air condition their buildings. In general, the more money people have, the more energy they use to How wealthy people in a country are) and energy use per capita. Map, and the low areas on one map are low on the other map.įor example, these are maps of GDP per capita (a rough estimate of That they look similar: the areas with high levels on one map are high on the other When we look at maps of two related variables, we will often notice However, it is also very easy to confuse correlation and causation.Īccordingly, correlation is also a useful but dangerous tool in theĪnalysis of spatial data. Home / Tutorials Geographic Correlation and CausationĬorrelation is "a relation existing between phenomena or things orīetween mathematical or statistical variables which tend to vary, beĪssociated, or occur together in a way not expected on the basis of chance alone"Ĭorrelation is one of the most common and useful tools in
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