# data[].attributes.body
I have changed the scale bar and the basemap, using a "World terrain base". After some research, I have found a better Amazon biome frame; I found out that what I used in the previous map was too similar to the  the Amazon basin, and not to the biome, that's why I have changed it. It was also possible to increase the class size of my legend, with the new Amazon biome. About the color scheme, I am still not sure, so I kept the same one.

# data[].attributes.attachment_caption
Based on the original considerations and remarks from others, I updated the map, toned down the decorative elements.

# data[].attributes.body
Hi everyone. My chosen indicator relates to research and development. It is a country's expenditure on research and experimental development (from companies, universities and state research institutions together). To make it comparable between countries, it is measured as % of local Gross-domenstic product (GDP). For example, 0.5% would be considered low, 3% and above would be considered high. It is an indicator for SDG9 (Industry, Innovation and Infrastructure), subtarget 9.5 (Enhance scientific research, upgrade the technological capabilities) and is seen as a measure of a country's ability to innovate. Since the state of technological progress determines the tools available to adress further challenges, R&D expenditure can be seen as an 'enabler' or 'catalyst' of many other SDGs. I chose it, because I used to calculate this indicator for Germany back when I was working at the Statistical Office, and I was always interested in how other countries do in that regard.

What I chose to map is the total difference between 1998 and 2018 in percentage points. A value larger than 0 means the relative importance of R&D in the economy has risen, a value lower than 0 means it has fallen. To show that, I decided on a divergent ratio scale with 0 as break point. My focus area is East Asia; I was interested in whether the change in that region would mirror the development of countries like China or South Korea from industrial to high-tech economies that we hear about in the news. I chose an equal-area projection (for a choropleth map) for the region: Albers Equal Area Conic Asia North.

EDIT: After some second thought, I chose to compare 1998 and 2018 by two maps using the same scale, rather than mapping the difference directly. I think it makes the interpretation easier.

EDIT 2: In the final map I did not include a scale bar, grid or numeric scale value, since the focus is strictly on thematic information (the countries and their assigned colours) and the geographic or navigational elements seemed to distract rather than add. Also, the scale is large enough to know where you are, presuming you have ever seen a globe.

# data[].attributes.body
I chose the indicator "Total greenhouse gas emissions (kt of CO2 equivalent)" which relates to the SDG 13 "Climate Action". The reduction of those emissions is a highly relevant topic especially in this and the following decades.
To be able to compare the different countries and to get "rate" as the level of measurement I decided to derive my indicator to "Total greenhouse gas emissions (t of CO2 equivalent) per capita". Therefore I used the "Population, total" indicator from the worldbank and calculated the emissions per capita for 1990 and 2018. These 2 new fields were multiplied by 1000 to get numbers > 0, so that the unit changed from kt to t. After that I created 2 maps: A first one showing the emissions per capita in 2018 and a second one showing the change in emissions per capita from 1990 to 2018. For this second map I needed to do another calculation ((emissions_2018/emissions_1990)*100%-100%) to get the percentage change over time. Because of the huge differences between the countries and to make it easier to read the map I decided to just show whether the emissions decreased (-x%) or increased (+x%) from 1990 to 2018.
I thought about choosing a focus area but I came to the conclusion that it is most interesting to see the emissions per capita worldwide, so I stayed with the world map. I used the Equal Earth projection, which is equal area, so that Antarctica for instance is not that huge like in the Mercator projection, because there is no data for Antarctica anyway.
For these two world maps I think map elements like north arrow, scale bar or grid are not necessary. The focus should be on the maps, titles and legends.

# data[].attributes.body
Hello everybody,
my chosen indicator is “access to electricity per country” (%) which corresponds to the SDG 7 “affordable and clean energy”.
After playing around with the data and reading parts of the UN 2020 “Mapping for a sustainable world”, where the proportion of the population with access to electricity is also mapped (p. 94), I decided not to further process the data as I aimed to concentrate on the continent with the most diverse distribution of access to electricity in detail. Therefore, I mapped the continent with the most recent data set of 2019 and adjusted the legend accordingly (white indicates not data), in order to gain detailed knowledge about the distribution of access. Since I focused solely on Africa, I decided to use the continent specific projection “Africa Albers Equal Area Conic” to avoid distortions as best as possible.

# data[].attributes.body
Hi everyone,
I chose the indicator "Forest (sq.km)" which relates to SDG “15- Life on Land". I focused on the Amazon rainforest, therefore I added the layer "Amazon Region"on my map and I used the tool "Pairwise clip" to extract the countries that overlay  the clip feature (the Amazon Region).
I decided to focus on this area since the deforestation of the Amazon rainforest is a huge problem that may lead to serious global consequences.
The level of measurement of my data is ratio-level; to show the decrease of the forest from 1990 to 2020, I used a formula to calculate the percentage change over time. As result, I obtained two values: positive and negative values. The former show the decrease of the forest and the latter the increase (I decided not to show these negative values, to avoid confusion, and I just wrote  "absent") .
I chose the Cylindrical Equal Area projection, to preserve the area along the equator.
I chose a continuous color scheme, but I reversed the color scheme, so the dark green colors show an increase of the forest and  lighter green a decrease.

# data[].attributes.body
The indicator I chose is renewable energy consumption as percentage of the total energy consumption. It corresponds to the SDG 7: “Affordable and Clean Energy”. The switch to renewable energies is important for example regarding climate change mitigation.
At first I mapped the state in 2018 (most recent data). But I also wanted to look at the development. For this I subtracted the values of the year 1990 (first year of data acquisition) from those of the year 2018. For the map I used a divergent colour scheme with zero as the central value. Thus you can see if the share of renewable energy increased (positive values) or decreased (negative values) in 2018 compared to 1990.
I kept the focus area global, so you can compare for example industrial and developing countries. I chose the Robinson projection, because it is a compromise between conformal, equal-area and equidistant display.
I did not include a north arrow, scale bar or grid since I do not think it adds any relevant information to my thematic map.

# data[].attributes.body
I chose the indicator "population living in slums (% of urban population)", corresponding to the SDG 11: "Sustainable Cities and Communities". I focused on the african continent; information from the year 2018 was used for the map. I chose to go with the projection area "Africa Albers Equal Area Conic" for a better representation of the continent and no visual distorsions.

# data[].attributes.body
my chosen indicator is grain yield which corresponds to the SDG 2 “zero Hunger”. I used a join to connect the table „grain yield 2018“ with the Dataset „World Countries“ and focused on differences between Africa and Europe. Because the unit of the grain yield depends on area , I used the Equal Earth projection, to show not only the different yield but rather the different area oft the countries. The Equal Earth projection  is a equal-area pseudocylindrical projection for world maps.

# data[].attributes.body
Hello, I chose the indicator "cereal yield (kg per hectare)" which relates to the SDG 2: "Zero Hunger". I looked at it globally and decided to compare the cereal yield of 1968 with 2018. I kept the range of the lower classes smaller, because the majority of the countries fell in this range. For the projection I used Equal Earth for equal areas, since the available area plays a big role. I didn't use a scale bar, because it wouldn't be beneficial for this global scale.

# data[].attributes.body
Hi everyone,
i chose the indicator „Forest area (sq. km)” which relates to SDG 15 “Life on Land”. With the map I would like to show the change in forest area between 1990 and 2020 of the individual states (worldwide) in relation to their total area. The change in forest area was calculated as a percentage for each country. I have tried to show the decrease in forest area between 1990 and 2020 with an intensifying red and the increase with an intensifying green. I used the "Equal Earth" projection to represent the continents without visual distortion.

# data[].attributes.body
Hi everybody,
I chose the indicator "Prevalence of Undernourishment (% of population). It is a good indicator to identify food distribution in the world, which relates to the SDG 2 "Zero Hunger", 3 "Good Health and Well-Being" and also gives an idea about 1 "No Poverty", since poverty and hunger are usually correlated. As for the goals 2 and 3: Hunger and malnourishment usually lead to poor health, which is why these two goals are tightly connected to each other in that regard. The map focuses on the difference between the years 2001 and 2019, but I added an overlaying layer showing the numbers from the year 2019 as a reference, because an increase in undernourishment is way more severe when the number was high to start with. I decided to show the whole world (excluding some islands and island states) because of the difference in industrial and developing countries. However, since most of the changes can be seen in Africa, I chose to use an adaption of the Aitoff (world) projection with a central meridian shifted by 25, which puts Africa in the centre of the map and puts more focus on the Southern Atmosphere.
Since the map is thematic and focuses solely on countries, I decided to exclude map elements and only put in a North Arrow to show that the adaptation did not change the North-South orientation of the projection.

# data[].attributes.body
I changed the projection from Equal Earth (world) to North Pole Lambert Azimuthal Equal Area since it will perform better for my map with focusing on the northern hemisphere area. Meanwhile, the color scheme was changed from green-red to red with the darkest red area showing the area with the highest value. Now with the new projection, the map looks more clearly in a larger square frame, and since the area is quite large I would keep one map instead of using two maps to keep the map being detailed as possible. Otherwise, it might be hard to view.

# data[].attributes.body
I chose the indicator "cereal yield (kg per hectare)" which relates to the SDG 2: "Zero Hunger". I looked at it globally and compared two years (distance of 20 years) with each other. I used Equal Earth for equal areas

# data[].attributes.body
I chose the indicator "Population Growth (% in 2020)" which is related to SDGlk 11 "Sustainable cities and communities". I found this indicator interesting because despite what most of the people think that the world is overcrowded actually for the first time in moderm history  the world´s population is expected to virtually stop growing by the end of this century. The replacement fertility rate that is the number of births per woman needed to maintain poluation size should be 2.1. The number of people 65 or older will increase, by the of this century the population in Europe (Germany, France, etc) Asia (Japan, China, etc..) will be cut by half . That will become an importat issue for goverments becasue how can a country be sustainable if they do not have young people to take the jobs, pay taxes and keep moving the economy. All the Goverments need to take action like more support for couples who want to be parents or promote the migration for people from other countries instead of stopping them. For that reason I decided to use a projection where all of us can see that this will become a problem for all the world.

# data[].attributes.body
Hello, My indicator is "total percentage of unemployment rate" which corresponds to the SDG 8: "Decent Work and Economic Growth". I focused on the middle east and North Africa region since this region is heavily affected with economical and political factors. Moreover, the region has diverse range of income levels and unemployement rates. I chose the projection "Egypt Extended purple belt" because it projects in the center of the region since the region is horizontaly wide. Egypt is the center of the region so I chose the projection at that place to try to preserve the area and shape of all the countries as much as possible. I didn't use grid because I thought the map will look too messy. I have 2 maps. The first map shows the number of years that has an unemployment rate more than 9.37. This value (9.37) represents the average of unemployment rate for all the countries of the regions between the years 1990 and 2020. The number of years indicates whether unemployment rate was considered a big issue in the country or not throughoout this period. In my second map, I wanted to see if there is a relation between the unemployment rate in 2020 and the country's income level. Since there are 4 different levels: High, upper middle, lower middle, and low income. I have dissolved the the countries with the same income by taking the average mean of the unemployment rate of these countries. I found out that there is no direct correlation between them, since the upper middle income level countries have the highest unemployment rate while the countries with high income have the lowest unemployment rate. Maybe the countries with low income are affected by political situations and that leads to have more vacancies of employments. I unified the color scheme of the 2 maps just to make it easier for the observer to compare.

# data[].attributes.body
I chose the indicator "Arable Land (% of land area) which relates to the SDGs "2. Zero Hunger" and "13. Climate Action". I looked at the change of Arable land across 20 years, by comparing the change in percentage of the value of the indicator in 2018 with 1998. I focused on Europe in particular because there were many countries that showed considerable decrease in Arable land, which shows the effects of climate change. I separated the results into two groups, based on whether the arable land increased or decreased since 1998. I assigned quantile ranges for the two groups separately and used different colours to highlight the change, green to show the increase and red to show the decrease. I used the Europe Lambert Conformal Conic projection because it preserves the shapes of the countries and there is not much distortion unless the region is closer to the south pole.

# data[].attributes.body
I have actually abandoned my previous considerations and chose a multivariate symbology (because I wanted to try it). I now have a visualization of Forest area in % and CO2 emissions in tons per capita on a bivariate scale. Both variables are classified according to three classes via quantiles - I don't really like it, but it gave the best visual result. The three colours for the two variables therefore represent the lower third, middle third and upper third of all possible values for the respective variable. Right now, I can't answer the initial questions, but I want to try an overlay in the next step as to not produce two maps next to each other - to finally show which countries are able to cover their emissions by their forest area!

# data[].attributes.body
Hi everybody,
My indicatior is GDP per capita (US$) which related to SDG 8 (decent work and economic growth). Here, I choose the data in 2009 since this time after global financial criris (2008) and the regions are Europe and Center Asia. The region in the map is horizotal wide since I wanna see the significant differece of between Europe and Central Asia. The projection I choose is Equal Earth (world) because I see it works quite well to cover the wide area. As the map shows, GDP per captita of Norway and Switzerland is the highest number even after crisis meanwhile Central Asia and other Europe contries took more the lower value.

# data[].attributes.attachment_link.readable_text
Number of data classes:

Nature of your data:

sequential diverging qualitative

Pick a color scheme:

Only show:

colorblind safe
print friendly
photocopy safe

Context:

roads
cities
borders

Background:

solid color
terrain

color transparency

BuGn

how to use | updates | downloads | credits

color advice for cartography

EXPORT

Export your selected color scheme:

Permalink

Share a direct link to this color scheme.

Adobe

Download an Adobe Swatch Exchange (ASE) file of this scheme.

GIMP and Inkscape

GIMP color palette for this scheme.

JavaScript

Colors for this scheme as a JS array

CSS

CSS classes for this scheme

# data[].attributes.body
Introduction https://www.youtube.com/watch?v=COf8isFlebE

# data[].attributes.body
hi everybody,
I chose the indicator "Net Total Social Expenditure, in % GDP" and "Time SPent in Unpaid Work by women, in min/day". I want to show the influence of social programs of the european states on the workload of women as part of SDG 10 "Less Inequality" and SDG 8 "Decent Work and Economic Growth", because mainly women are still compulsrily responsible for the unpaid care work in our society.
I used graduated colours for the social expenditure and represented the workload in sizes of circles (starting with 220 min/day to >300 min/day)