My favourite dataset in the last 30 days map challange is the one I found in Kaggle website about US accidents (10M records). The map below shows accidents in Chicago by hour (height by count) between 2016 and 2019. 

Car accidents are still one of the biggest challenges in our growing cities. You can see that in the evening more accidents are in the western side of the city, and in the morning in the city centre + they are concentrated along the main city arteries. I am sharing the link to the interactive version in the comments. Try to answer the question of why there are almost no accidents between the road arteries in the East. And I am wondering what do you think - how can we solve the problem of accidents in cities in the near future?

Map presents population density with hipsometry overlay in 3D. India and Bangladesh have already very high population density, are very vulnerable to sea level rise, typhoons & floods. They are also predicted to grow its population until 2050. All of above will drive international migrations, tensions, new possibilities and transfer of knowledge and culture.

 Has the race for alternative energy even started? Map shows dynamics in CO2 emissions by plants (data gathered by Beyond Fossil Fuels). As you can see changes on the continent are not as spectacular as in UK. Some of the biggest polluters even expanded its emissions. Most of the emissions are stable between years. The biggest falls are recorded in Belgium (2015-2020), next to Brussels. Take into account that 2020 was a first year of COVID19 in Europe - the demand for power was lower then it is now.

4.7M accidents in USA by month agreggated to h3 hexagons centroids since 2020. Interactive animation shows car accidents, colored by month. Many lives are taken by car accidents every year. We are heading fast towards autonomous vehicle future. Will it be an accident-free future?

Wildfires are fierce and devastating events. We can estimate its cost in $, but loss of human lives & loss in our biosphere are beyond this scale.

Black vs white, good vs bad, energy vs natural night, people in darkness vs people in light. The map shows #NASA Black marble night lights (image taken in October 2023) against 100K+ population hexagons (23 km radius). You can clearly see there bright lights in Russia even without a big concentration of population. Compare it to Ukraine, where we have full-scale war since 24 February 2022 - Most of the hexagons do not have a lot of light inside. Night lights can show places where there are fires, concentration of cities' lights (money spent on energy there) or lights that are not related to cities, but e.g. with mining, or industrial production.

Black vs white, good vs bad, energy vs natural night, people in darkness vs people in light. The map shows #NASA Black marble night lights (image taken in October 2023) against 100K+ population hexagons (23 km radius). You can clearly see there bright lights in Russia even without a big concentration of population. Compare it to Ukraine, where we have full-scale war since 24 February 2022 - Most of the hexagons do not have a lot of light inside. Night lights can show places where there are fires, concentration of cities' lights (money spent on energy there) or lights that are not related to cities, but e.g. with mining, or industrial production.

3D data opens up many possibilities. We can use 3D terrain maps and buildings to map floodplains, storm surges, tsunami and other catastrophes that require 3D modelling and datasets. Most of all, it opens up imagination, making our maps and data analysis more approachable for everyone. I used Unity 3D to prepare the dataset and animate camera movement and plane.

This is a screenshot from my first game - Earth Quiz. It is extremely difficult to orient ourselves in this perspective - try to find Shanghai, Hanoi or New Delhi. You can get the game at Google Play.

Location planning. The series of show how to find a perfect place - in which rivers, forests and restaurants is minimized. It is a power of rasters - we can generate continous distance rasters and just add them. Result can be also continous value. Possibilities are endless here.

I took all my gpx activities from Strava in Krakow, and mapped them and changed date to 20 XI 2023. Every point that you see on the animation is my activity in certain time during 24 hours. Feel free to explore! Guess where I lived, worked, ran or meet with friends.

Our mobile phones currently spy on us. If you know someone's location witht this precision, you know where he lives, who is his friend, where he works, if he is active person or not. Imagine what happens if you add sound recordings and photos that our phones store.

Earthquakes are still one of the most unpredictable natural hazards on Earth. They can be very deadly especially in areas that are not prepared for it. Animation shows more than 100 years of earthquakes in California (USA) mapped agaist current population.

Rise and fall of hurricane Ian. Hurricanes/typhoons are one of the most destructive forces at our planet. They are predicted to become even more powerful at warmer Earth. Film presents category 5 hurricane Ian, that hit USA in September 2022. These atmospheric beasts take power from ocean and wind and are weakened as move over land.

Estimated internal migrations in Africa. 

Although 71% of Earth's surface is covered by oceans, we did not map its seabed. Deep oceans hide polymetallic nodules with metals crucial for energy transition, as well as species that we still did not discover.

400,000 city Mariupol was one of the first destroyed during the Russian invasion of Ukraine on 24th February 2022. It is reflected in Open Street Map data. Map shows points and buildings as points edited by OSM contributors before the invasion (red dots), and after (blue). 91 thousand points were mapped before (since 2011, max on May, and June 2020), and only 4.4 thousand after. However, according to Guardian: "46% of the city’s buildings were damaged or destroyed in the siege". The lack of updates in this place tells its own story about the war.

Explore interactive version of the map.

Coronavirus pandemic changed Europe and brought deaths to 1.7 million people more than we would expect in normal period.  The map shows a ranking based on excess death statistics since it is the most comparable one across countries and it is more reliable than the number of deaths officially attributed to coronavirus (Eurostat 2023; link below). Excess deaths here are deaths from all causes in the COVID-19 period above the average of deaths before the coronavirus pandemic (2016-2019). The higher the value, the more additional deaths. If the value is negative, it means that there were fewer deaths than in the 'normal period'. This measure is far more comparative than deaths officially attributed to coronavirus for many reasons: hospitals reported coronavirus deaths differently (there might be some incentives to classify someone as a "covid patient), the virus could be one of many reasons why someone died or deaths may be related to country's restrictions (e.g. patient may skip some diagnosis, hospitals were not accepting as many patients as usual).

Coronavirus ranking: the worst countries to live in during the coronavirus pandemic were Bulgaria, Cyprus, Poland and Slovakia - there were more than 19% excess deaths during the 2020-2022 period. On the other side of the ranking are: Denmark, Finland, Iceland, Norway and Sweden, with excess deaths lower than 7%. Many questions need to be answered: were the lockdowns, restrictions on movement and gathering the right decision, how vaccinations impact excess deaths, what was the role of geography, our culture & trust in the results, what the statistics cannot tell us... and why Sweden, with its controversial&liberal strategy, won?

Explore interactive version of the map and web application with much more statistics to understand country stories.

Issues of transboundary aquifers might lead to conflicts and wars. Especially in a warming world, in which clean water resources will become more valuable in some areas than they were before. The quantity of water determines how much water we can use to irrigate fields or how much power we can generate with our water power plants. Quality of water impacts human lives, agriculture and fishing industries. Usually, the power of a country determines its position in managing an transboundary aquifer.

Deforestation of Amazonian rainforest pose many questions and is a part of global system. Map shows commodity-driven large-scale deforestation, mainly due to commercial agricultural expansion (within 10 km grid). Who should be responsible for this deforestation? People who drive demand for agriculture in Amazonian rainforest from all around the world? Or South American governments? Explore more at these sites: Global Forest Watch, NASA Earth Observatory or Earth.org.

Hipsometry maps are retro & crucial during wars. Today we have an Independence Day in Poland - we became again one country after 123 years of occupation by Russia, Prussia and Austria. The map shows Crimea Penisula, a place where in fact the current ongoing war in Ukraine has started. 

The map shows 2 days of animated #flights in September 2019 mapped against #population hexagons and 2 million+cities. It was created within 2 hours because I used well-prepared #opensource datasets. I think that the accessibility of open, well-structured, organized data is something that North America has, and the rest of the world is striving to achieve in XXI century.

How the map was created? I just used kepler.gl demo data for flights, changed visualization style and added my data about population

In 2022 we reached 8 billion people living on this planet. We will be 9.7 billion in 2050. "China’s population is projected to decrease by 48 million" between 2019-2050 (World Population Prospects 2022). Growth is not equal. What does it mean for our future?

We are heading towards a net-zero future forgetting that it will put back people from Africa. The cobalt belt in DR Congo is one of the most controversial areas in the world - next to big open pit mines, there are small, illegal "artisanal" cobalt mines, where children and freelancers work in harsh and toxic environments for a few dollars a day. DR Congo extracts 63% of Cobalt globally, while 60% of the production takes place in China. Cobalt is needed for battery production (e.g. for electric cars). It is a critical raw material marked by the EU study - vital for our economy. We cannot get back from a net-zero future, and we cannot fuel Russia and other countries with petrodollars, but we can think of human rights, economic equality, battery alternatives and on-site processing plants in Africa before we recharge.

Oceans biodiversity can collapse because of people on land. The map shows density of cargo, tankers and fishing ships stacked together with marine protected areas. Growing demand for goods, fuel and marine food clash with nature. Map shows area around USA, a place that experience marine dead zones, overfishing conflicts as well as it witnessed many ecological catastrophes connected with oil spills. It is also one of the largest world economy, with cargo traffic around coral reefs ("underwater rainforests") in Caribbean Sea.

How the map was created? I have downloaded around 50 gb of data from AIS Broadcast Points from marine vessels - in total around 1.5 billion rows of data (size of this data was the biggest challange). I selected only cargo, tanker and fishing ships - getting down to 500K rows. I counted points within h3 hexagons per day and visualized it in kepler.gl next to marine protected areas. In the end I made a film with Blender.
Challanges: 1) 50 gb cannot be easily read by Python. You need to do it file by file (every file got around 1 gb) 2) program could not read some files because of tokenizing error, I solved it with skipping wrong rows of data df = pd.read_csv(file, on_bad_lines='skip'). Nevertheless I would handle it diferently if it was not just for visualization contest.

Typhoons are the most powerful and severe weather events. In our warming world typhoons are going to hit harder. The map presents the last 50 years of typhoons in Asia and compares it to nowadays population living in its megacities, with the intention to think about our future resilience, inequalities and solidarity.

(explore interactive version)

How the map was created? I clipped h3 Kontur.io population data to biggest h3 hexagons that covered typhoon zones. I clipped tyhoon dataset manually in QGIS. I selected significant columns and plot it in kepler.gl.

We have between 1.2M - 2M Ukrainians in Poland, mostly in the biggest Polish cities. Since the first days of Russian invasion Polish society opened their hearts for Ukrainians, hosting them in their homes, welcomed in our country, helping in transfers and in setting up new life. It was one of the most beautiful act of our solidarity since 1989. This analog map is tribute to all the Polish people who stand with Ukraine in this war. The biggest 'thank you' is for all Ukrainians who fight for freedom.

I made 2 serious mistakes on this map +1 that you can forgive +at least 2 more minor ones. Can spot them?

The map shows access risk to health facilities. In a warming world pandemics like Covid-19 one will be more frequent. Accessibility to doctors might be an vital factor in our survival. This kind of accessibility measure is usually hard to interpret without playing with the dataset. Nevertheless it might be insightful for location planning purposes. This measure is built on travel time to the two closest health facilities and estimated usage of health facilities. Details are in the link below. An interesting fact is that African countries have a lot of very granular datasets, that can be used for data visualization and analysis. They are published for free to boost development on this continent.

The map below is a slice of my agent based model, that I did for my PhD. It was the most complicated task I ever did. You can see location of agents (simulated population), who were choosing swimming pools for a daily training. If there was no place at the time they came there, they were unhappy and they got back home, and next day they changed their rating of swimming pools in the city and tried next one. The simulation was repeated thousands of times, to get statistically significant results. All this was created to get to parameters that described a model that could be used in the future in planning of new swimming pools (or alternativelly closing ones we do not need). This was a bottom-up method that was later on compared with spatial interaction model in my Phd. If you want to explore it further, here is a link to my paper about it: https://www.jasss.org/22/1/1.html 

Coronavirus pandemic first stroke Southern Europe in 2020. The most reliable statistics to investigate death toll of it is excess death (in red) since it is the most comparable one across countries and it is more reliable than the number of deaths officially attributed to coronavirus (Eurostat 2023; link in comments). Excess deaths here are deaths from all causes in the COVID-19 period above the average of deaths before the coronavirus pandemic (2016-2019). The higher the value, the more additional deaths. This measure is far more comparative than deaths officially attributed to coronavirus for many reasons: hospitals reported coronavirus deaths differently (there might be some incentives to classify someone as a "covid patient), the virus could be one of many reasons why someone died or deaths may be related to country's restrictions (e.g. patient may skip some diagnosis, hospitals were not accepting as many patients as usual).

Explore interactive version of my web application with much more statistics to understand country stories (coronavirus ranking [loads slowly])