![]() You have three tries to select the correct country. The game begins asking you to click on the country of France. The more countries you correctly select, the higher the score you will get. color_mapper = LinearColorMapper(palette = palette, low = 0, high = 40) #Define custom tick labels for color bar.The object of the game is to select the correct European country in as few of guesses as possible. palette = palette #Instantiate LinearColorMapper that linearly maps numbers in a range, into a sequence of colors. palette = brewer #Reverse color order so that dark blue is highest obesity. geosource = GeoJSONDataSource(geojson = json_data) #Define a sequential multi-hue color palette. ![]() ![]() from bokeh.io import output_notebook, show, output_file from otting import figure from bokeh.models import GeoJSONDataSource, LinearColorMapper, ColorBar from bokeh.palettes import brewer #Input GeoJSON source that contains features for plotting. We are now ready to render our choropleth map using Bokeh. merged_json = json.loads(merged.to_json()) # Convert to String like object. We therefore convert the merged file to GeoJSON format. GeoJSON describes points, lines and polygons (called Patches in Bokeh) as a collection of features. Bokeh consumes GeoJSON format which represents geographical features with JSON. However, since we want to incorporate data visualization interactivity, we will use Bokeh library. The merged file is a GeoDataframe object that can be rendered using geopandas module. merged = gdf.merge(df_2016, left_on = 'country_code', right_on = 'code') The resulting dataframe df_2016 can then be merged to the GeoDataframe gdf. This requires filtering data for year 2016 from df. Let us first create a static map representing Share of Obesity in Adults in the year 2016. To keep it simple, let us ignore the missing data for Sudan. This resulted in a change of 3-letter ISO code for the newly split country. Our data spans the period from 1975–2016 while Sudan split into two countries in July 2011. Investigating pandas dataframe shows missing values for Sudan. import geopandas as gpd shapefile = 'data/countries_110m/ne_110m_admin_0_countries.shp' #Read shapefile using Geopandas gdf = gpd.read_file(shapefile)] #Rename columns. The columns are renamed for easy referencing. The shapefile consists of many columns, of which only 3 are of any interest to us. ![]() Geopandas can read almost any vector-based spatial data format including ESRI shapefile using read_file command which returns a GeoDataframe object. Alternatively, feel free to download the files from my github repository. Next, go to Our World in Data and download the share-of-adults-defined-as-obese.csv by clicking on Data tab on the plot. Click on the green button Download Countries v4.1.0 to download the shape folder. For our purpose, 1–110m small scale data is good enough. Natural Earth is a public domain map dataset that provides geospatial data at various resolutions. To render a world map, we need a shapefile with world coordinates. The entire code can also be found at my. In the first section, we will create a static map and then later build on our code to introduce interactivity. In this step by step guide, we will recreate an interactive global choropleth map on Share of Adults who are obese (1975–2016) using Python libraries and package - Pandas, Geopandas and Bokeh. However, creating a dynamic map is slightly tricky and that is exactly what we are going to learn in this blog. There are many tools and packages available to make a stand alone or static choropleth map using Python. The geographical area may expanse the entire world, or a country, state or even a county. Choropleth Maps display divided geographical areas or regions that are coloured, shaded or patterned in relation to a data variable. I was intrigued with this wealth of information and motivated to dive deeper.Ī quick google search led me to choropleth maps. These visualizations help us understand how and why the world has changed over the last few decades. A Complete Guide to an Interactive Geographical Map using PythonĮver wondered how these beautiful geographical maps are created? Our World in Data has an extensive collection of interactive data visualizations on aspects dedicated to the global changes in health, population growth, education, culture, violence, political power, technology and several things that we care about.
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