![]() This was more of a sandbox session in colour palettes and rasters to bring out some of the super intricate detail of terrain models, as well as improving my text and label positioning in R. Leaflet pacman::p_load(here,dplyr,rworldmap,leaflet,readr,rgeos,purrr,stringr,ggthemes,showtext,geosphere,htmlwidgets)ĭay 9: Monochrome Exploring the Appalachian TrailĮxploring digital elevation models (DEM) of the Appalachian Trail, USA, with the parts along the range where I’ve visited for either camping or hiking during 2018–2020. However, a time series component seemed like overkill for this particular exercise. Australia is in the top five major exporters for honey. Mapping Australia’s honey exports from some publicly available trade data for 2017. Mapbox pacman::p_load(here,mapdeck,dplyr,purrr,readr)ĭay 8: Yellow Australia’s global honey export trade Press the down arrow or use the up/down webpage scroll bar if the legend is chopped off. There are probably species differences between the cinnamon and red varieties. I’m no squirrel expert, but I’ve seen these squirrels in person/in squirrel and they look pretty red to me. There are detailed behaviour data too, but location data are fine for this exercise. I’ve seen these data used many times and I hadn’t tried them yet. Squirrels! The NYC Open Data Squirrel Census on squirrel sightings. Mapbox pacman::p_load(mapdeck,readr,ggmap,dplyr,sf,sfheaders,data.table,tigris,sp,maps,colorspace)ĭay 6: Red Cinnamon squirrel locations in NYC Central Park Georeferencing the data didn’t find all locations, so some points are missing.Ītlanta, USA (where I lived during this time). ![]() There is a higher density of destination sites because I primarily used Lyft to get home, which is concentrated on one latlon point.The data here are too sparse to make full use of this. Hexagons are good for large scale coarse and clustered data, like heatmaps.Note the legend in the below images in case the legend in the link is chopped off.Cities with labels contain data, sometimes only a few points. ![]() Zoom out to see the cities where I used Lyft to get around.Data were first georeferenced to get latlons.Data were obtained from my Lyft ride report.There is also a time component, which I’ll definitely use for another analysis. My data here ended up being too coarse (obviously I didn’t take enough Lyft rides) to leverage this, but it tells a story about where my ride activity is weighted. Hexagons are good for visualising frequency and mobility spatial data. These data are really cool, so I just wanted to make use of them. Using geolocation data for my Lyft rides as a passenger to build an interactive map that shows my Lyft activity, including origin pickup and destination dropoff points. R pacman::p_load(here,sf,RColorBrewer,dplyr,ggmap,sp,maptools,scales,rgdal,ggplot2,jsonlite,readr,devtools,colorspace,mapdata,ggsn,mapview,mapproj,ggthemes,reshape2,grid,rnaturalearth,rnaturalearthdata,ggtext,purrr)ĭay 4: Hexagons Mapping my Lyft ride activity over two years Polygon data were retrieved from Natural Earth Map data. This map is using geolocation data to track their pathway across the US with R and Mapbox.ĭata were georeferenced from mobile location data using Open Street Map. I met them in Memphis, then drove to ATL. They roadtripped the south, starting in Austin. ![]() My parents visited the US/me when I was living in ATL. Mapbox pacman::p_load(here,sf,RColorBrewer,dplyr,ggmap,sp,maptools,scales,rgdal,ggplot2,jsonlite,readr,devtools,colorspace,mapdata,ggsn,mapview,mapproj,ggthemes,reshape2,grid,rnaturalearth,rnaturalearthdata,ggtext,purrr) (Best viewed full screen switch browsers if loading time is slow)
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