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Rising Star Award

Community Care Award

Best Design Award

Technical Award

Global Impact Award

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2025 International Humanitarian Mapathon Avishan Bodjnoud Keynote

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2025 International Humanitarian Mapathon Avishan Bodjnoud Keynote

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Avishan Bodjnoud, Chief of the Information Management Unit in the Office of the Director for Coordination and Shared Services at the UN’s Department of Political and Peacebuilding Affairs–Department of Peace Operations, delivered the keynote address at the 2025 International Humanitarian Mapathon. Drawing on her personal journey and global experience, she spoke powerfully about the role of digital volunteerism and the critical importance of open data and mapping in supporting vulnerable populations. Her message highlighted how these tools not only inform humanitarian action but directly impact and save lives around the world.

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2025 International Humanitarian Mapathon Avishan Bodjnoud Keynote

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From Crisis to Action: Mapping Climate Disaster & Policy

USC

Mari Jorge

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SHAKING THE ARCHIPELAGO
25 years of Seismic Activities in the Philippines

University of the Philippines

Ariel Dome, UPRI Youthmappers - UP Diliman

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Targeting intervention: addressing regional healthcare disparities in Indonesia

Hitotsubashi University

Chiara Binello

Displacement of population in East-DRC

Hitotsubashi University

Auriane GOURANTON, Domitille GUERARD, Malina JOUANNEAU

Educational Vulnerabilities in Myanmar

Hitotsubashi University

CHEN Luijing, HUBAULT Jules, LANCE Mathilde, MAZY Celia

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2024 - Water Crisis in Sicily - Italy

Hitotsubashi University

Alessandro Piazzoni Marinetti, Mike Perko, Timnah Olcha

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仲良くなりましょう！

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Hello everyone !

I’m Yamazaki .

Nice to meet you.

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My name is Haruta!

I go to Tokyo denki University!

It’s first time Mapathon for me!

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This map pins out all the earthquakes that have occurred in Japan so far, and when you press a pin, detailed information appears.

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This is a world map showing information about earthquakes. This map visually represents the magnitude of events through a color gradient, ranging from blue to red. Additionally, a coordinate search function is available in the upper right corner of the screen, allowing users to easily locate specific points.When you select a point, information about other nearby points appears, allowing you to compare them.

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🗺️ 日本語チュートリアルはこちらからご覧いただけます：

https://bit.ly/reitaku

📌 マッピング対象のタスクはこちら：

https://tasks.hotosm.org/projects/19113

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My name is Kat! I’m an environment studies student focusing on public policy at USC! I love the intersection GIS brings!

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Hello, I’m Michelle Li, one of the Humanities and Social Sciences librarians at UCLA. My subject areas are Anthropology, Communication, and Linguistics. Here to support Maggie (right), and Miki Goral’s here (middle) too!

今年もよろしくね！

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I’m Maggie, one of the UCLA organizers. We are underway and having fun!

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Hello! I am a GOS and digital humanities specialist with OARC at UCLA

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千葉県柏市光ケ丘２丁目１−１ 麗澤大学

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When Crisis Strikes the Field: Stadiums Lost to Disasters and Conflicts

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When Crisis Strikes the Field: Stadiums Lost to Disasters and Conflicts

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This project visualizes stadiums around the world that have been damaged, abandoned, or rebuilt due to natural disasters or conflicts.
I chose this theme because my favorite soccer team's stadium was severely impacted by the Great East Japan Earthquake and had to be rebuilt.
Losing a stadium full of memories, traditions, and history was heartbreaking for me.

This map shows over 100 stadiums affected by earthquakes, storms, floods, wars, and other crises.
Each point includes the type of disaster, year, damage description, and current status (e.g., abandoned, repaired, or rebuilt).
The data reveals how vulnerable even iconic places are — and how recovery is uneven across regions.

Through this map, I hope to highlight how disasters impact not only infrastructure, but also communities, identities, and the spirit of the people.

Map created with Kepler.gl using a custom dataset of affected stadiums.

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Escalations in the South China Sea are increasingly evident through China’s construction of militarized artificial islands and the systematic deployment of maritime militia vessels across contested waters.

This map highlights key artificial island groups and demonstrates how China leverages them to assert dominance over the region, often within striking distance of neighboring states such as the Philippines.

According to the AMTI’s 2024 analysis, the Chinese maritime militia is involved in sustained, coordinated swarming operations around critical flashpoints close to the borders of other contestants, like Second Thomas Shoal and Hughes Reef. This is an attempt of hard power projection through military outposts and maritime patrols.

The map aims to visualize these dynamics by displaying

Artificial Island groups, coded by the increase in Chinese maritime militia activity

Key Island groups have been labelled

Martime Boundaries, solid lines representing the EEZ under UNCLOS and highlighting overlapping zones essentially the Nine-dash line

The proximity of Chinese artificial islands to neighboring states like the Philippines and Vietnam highlighting strategic military encroachment

Resources used

https://amti.csis.org/island-tracker/

https://amti.csis.org/dropping-the-act-chinas-militia-in-2024/

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2024/08/22–2024/09/01 Typhoon No.10: Trajectory and Rainfall Changes, Distribution of Schools and Evacuation Centers in Kanagawa Prefecture’s Flood Inundation Risk Areas(2024/08/22–2024/09/01 Typhoon10 進路と降水量変化、神奈川県洪水浸水想定エリアと学校・避難所の分布)

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2024/08/22–2024/09/01 Typhoon No.10: Trajectory and Rainfall Changes, Distribution of Schools and Evacuation Centers in Kanagawa Prefecture’s Flood Inundation Risk Areas(2024/08/22–2024/09/01 Typhoon10 進路と降水量変化、神奈川県洪水浸水想定エリアと学校・避難所の分布)

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In this project, I visualized the progression of Typhoon No. 10 in 2024, including its trajectory and rainfall changes over time, and overlaid schools (pink points) and evacuation centers (camp icons) on flood inundation risk areas in Kanagawa Prefecture.
Focusing on the spatial relationship between schools and evacuation centers, I aimed to explore practical challenges in evacuation planning during disasters.
The visualization revealed that in several high-risk areas, there are schools from which it would likely be difficult to reach an evacuation center safely on foot.
This analysis suggests that the common assumption—that immediate evacuation from schools is always feasible—may not hold true in all regions. By overlaying flood risk areas and evacuation facility locations, the project enables intuitive understanding of regional vulnerabilities.
Through this visualization of disaster risks and impacts, I aim to contribute to the reassessment of evacuation plans, the improvement of disaster infrastructure, and the enhancement of community disaster awareness.
By combining mapping with time-series data, the project provides a visual narrative that encourages reflection on both risks and resilience.

References

https://nlftp.mlit.go.jp/ksj/gml/datalist/KsjTmplt-N10-v1_1.html

https://nlftp.mlit.go.jp/ksj/gmlold/datalist/gmlold_KsjTmplt-N01.html

https://www.geospatial.jp/ckan/dataset/notifude-14kanagawa

https://disaportal.gsi.go.jp/hazardmapportal/hazardmap/copyright/opendata.html

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2024/08/22–2024/09/01 Typhoon No.10: Trajectory and Rainfall Changes, Distribution of Schools and Evacuation Centers in Kanagawa Prefecture’s Flood Inundation Risk Areas（2024/08/22–2024/09/01 Typhoon10 進路と降水量変化、神奈川県洪水浸水想定エリアと学校・避難所の分布）

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2024/08/22–2024/09/01 Typhoon No.10: Trajectory and Rainfall Changes, Distribution of Schools and Evacuation Centers in Kanagawa Prefecture’s Flood Inundation Risk Areas（2024/08/22–2024/09/01 Typhoon10 進路と降水量変化、神奈川県洪水浸水想定エリアと学校・避難所の分布）

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In this project, I visualized the progression of Typhoon No. 10 in 2024, including its trajectory and rainfall changes over time, and overlaid schools (pink points) and evacuation centers (camp icons) on flood inundation risk areas in Kanagawa Prefecture.
Focusing on the spatial relationship between schools and evacuation centers, I aimed to explore practical challenges in evacuation planning during disasters.
The visualization revealed that in several high-risk areas, there are schools from which it would likely be difficult to reach an evacuation center safely on foot.
This analysis suggests that the common assumption—that immediate evacuation from schools is always feasible—may not hold true in all regions. By overlaying flood risk areas and evacuation facility locations, the project enables intuitive understanding of regional vulnerabilities.
Through this visualization of disaster risks and impacts, I aim to contribute to the reassessment of evacuation plans, the improvement of disaster infrastructure, and the enhancement of community disaster awareness.
By combining mapping with time-series data, the project provides a visual narrative that encourages reflection on both risks and resilience.

References

https://nlftp.mlit.go.jp/ksj/gml/datalist/KsjTmplt-N10-v1_1.html

https://nlftp.mlit.go.jp/ksj/gmlold/datalist/gmlold_KsjTmplt-N01.html

https://disaportal.gsi.go.jp/hazardmapportal/hazardmap/copyright/opendata.html

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Considering the Forest Fire in Ofunato City, Iwate Prefecture and the Challenges of Evacuation Shelters

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Considering the Forest Fire in Ofunato City, Iwate Prefecture and the Challenges of Evacuation Shelters

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This storytelling map was created using Google Earth. It visualizes the spread and containment of the fire over time by marking the daily occurrence of fire outbreaks with color coding. It also maps the evacuation centers within Ofunato City. The mapping revealed that there was only one evacuation center located in the area where fires were heavily concentrated, while most evacuation centers were clustered farther away from the fire origin. As water and power outages threatened essential infrastructure and forced many residents to seek shelter, the uneven distribution of evacuation centers emerges as a significant issue.

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Bear Incident and Countermeasure Sites in Akita

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Bear Incident and Countermeasure Sites in Akita

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Project Title:

Bear Encounters and Human Injuries in Akita Prefecture
– Toward Measures Against Bear-related Damage

Explanation of Project:

In this project, we focused on Akita Prefecture, which recorded the highest number of bear-related human injuries in Japan, and visualized the crisis of bear encounters using Google Earth in a storytelling format.

We mapped the locations where human injuries caused by bears occurred, as well as the locations where Akita Prefecture conducted outreach workshops aimed at preventing such incidents.

We also attempted to visualize the locations of electric fences installed to prevent bear intrusions. However, after contacting the Wildlife Management Team of the Nature Conservation Division, Department of Living Environment, Akita Prefecture, we learned that detailed location data is not centrally managed, as fences are installed individually by private entities. Therefore, we focused on mapping outreach workshop sites.

Through this map, we aim to share the efforts made by local communities toward coexistence with wildlife. 🌲🐻

Map / Application Link🔗:

👉 https://earth.google.com/earth/d/1z5HweGPRlPcWTM-1v6gyQU9zLZTsDE66?usp=sharing

Data Visualized on the Map🗺️:

Locations of human injuries caused by bears in Akita Prefecture

Locations where outreach workshops for bear countermeasures were conducted

(Note: Locations of electric fence installations were not visualized due to unavailable data)

Supporting Visuals📸:

Mapped points of bear-related human injury sites and outreach workshop sites

(No additional visual materials beyond the map itself)

Team Name and Members👫:

Team Name: MAPPINGERS
Members: Kentaro, Chisato, Kouna, Shota

Location📍:

Akita Prefecture, Japan

University🏫:

Aoyama Gakuin University

Sources📚:

Akita Prefecture. (2024). List of Human Injuries Caused by Asiatic Black Bears in FY2024.

Akita Prefecture. (2024). Bear Information Mapping System (KUMADAS).

Futto Elementary School, Oga City. (2024). Outreach Workshop to Prevent Bear-related Damage.

Akita Prefectural Omagari Agricultural High School Website. (2024). Report on Outreach Workshop: "Ecology and Damage Countermeasures for Wildlife" by Akita Prefectural Government.

Forest Akita. (2023). Forest School 2023: Thinking About Bear Problems.

Yahoo! News. (2024). News Article on Bear Countermeasure Drills.

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With creation tools, you can draw on the map, add your photos and videos, customize your view, and share and collaborate with others.

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I planned to use coordinate data from various countries to plot EMDAT data on a map, and from there, further overlay geographical data such as support data for each country, ocean currents, and tectonic plates.

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This app visualizes the earthquakes that have caused significant damage in Japan since 2000 using augmented reality (AR). By utilizing three-dimensional depth information, which cannot be visually represented on a 2D map, the app allows for a spatial visualization of the earthquake's scale.

Additionally, the size of the circles is adjusted according to the depth and magnitude of the earthquake, enabling users to intuitively grasp the scale of the earthquake. Tapping on the epicenter displays detailed information about the magnitude, and the smartphone's vibration corresponds to the magnitude, allowing users to physically sense the strength of the earthquake.

This app introduces a completely new form of mapping, offering an innovative experience that enables a more intuitive and tactile understanding of earthquake information.

Download Link：https://testflight.apple.com/join/rCud2wdg

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Syria War (Aftermath?): Civilian Fatalities Study

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Syria War (Aftermath?): Civilian Fatalities Study

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Comparison of Internal vs External Actors contributing to the deaths caused

Utilising a timeframe from October 2023 to 18 April ( Escalation in Israel's strikes after the Israel-Hamas attack), we believe that Internal Actors (specifically Assad's government) has contributed to significantly more civilian fatalities than External Actors (like Israel's government). Top 4 attacks (In terms of number of casualties) : anti-militias executions conducted by the Syrian - likely done to repress anti-Assad forces within the civilian population

In the map, the scale of Syrian casualties is represented by the size and the colour of the circle where the larger and yellower it is, the larger the scale. In Damascus, on first view, as the red dots (representing Israeli air strikes) are concentrated in the larger circle, we may think that the strikes have delivered a larger number of fatalities. However, on further analysis of the data, we see instead that internal state repression may have caused more fatalities.

Although there were many bombings over time which yielded a significant amount of civilian fatalities, but a 1:1 comparison of fatalities caused by Israeli bombings vs State violence would skew in the latter favor, due to the targeted groups in both militias being the State of Syria in the former, and the Syrian civilian population in the latter.

Israeli bombings were targeted at state of Syria - contained within geographical regions with little civilian population. Attacks were surgical in nature, against military and military infrastructure. Purposes were meant to weaken any surrounding threats around Israel and for preventing any weapons from falling into the hands of the militia.

More research should be done to investigate the causes of Syrian civilian deaths besides the factors already mentioned.

Map link:

https://kepler.gl/demo/map?mapUrl=https://dl.dropboxusercontent.com/scl/fi/7uzpn0xgvczkx8ls7ly8g/keplergl_nhutp6l.json?rlkey=0m220jxu0w9v2wovx7hdeqw1q&dl=0

Sources:

Data Set for Anti-Civilian Violence was extracted from here

https://acleddata.com/curated-data-files/

https://www.france24.com/en/middle-east/20250226-israel-syria-military-netanyahu (Context on why Israel continues to strike Syria, intensifying after Assad is thrown out in the coup)

https://acleddata.com/2024/12/19/syria-israeli-airstrikes-reach-an-all-time-high-after-assad-regime-falls/

https://www.aljazeera.com/news/2024/10/12/how-can-israel-attack-syria

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Crop damage from Tropical Cyclone Michaung

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Crop damage from Tropical Cyclone Michaung

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Mapping the effects of 2023 Tropical Cyclone Michaung on crops. Specifically, the region of South Coastal Andhra Pradesh in India. The crop inundation and precipitation are mapped and plotted according to region and marked in severity by color.

Normal average precipitation across the geographic blocks studied for the 3 day time period was .9mm. There was an average rainfall from Cyclone Michaung of 138.7mm from December 4-6th resulting in a 14650.5% deviation.

A total of 39413 acres of crops were inundated by the rains in the area studied, about 35 thousand of which were paddies.

This map presents a better visual representation of the effect of heavy rainfall on farmers and their crops.

Sources:

Narasimha, Raja & Reddy, Venkata & Prasad, P N Siva & Raju, Krishnam. (2024). Impact of michaung cyclone effect on different crops in N.T.R district of South Coastal Andhra Pradesh. International Journal of Research in Agronomy. 7. 437-440. 10.33545/2618060X.2024.v7.i4f.601.

Global Disaster Alert and Coordination System. (n.d.). Overall Orange alert Tropical Cyclone for MICHAUNG-23. GDACS - Global Disaster Alert and Coordination System. https://www.gdacs.org/report.aspx?eventid=1001037&episodeid=1&eventtype=TC

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Crop Flooding in India from Cyclone Michaung 2023

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Chiba Prefecture flood damage forecast map

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Chiba Prefecture flood damage forecast map

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Data foreseen tsunami flooding is shown.

The largest estimated river flood inundation data is then shown. They also show areas where flooding is expected, such as house collapse (river bank erosion) and areas where flooding is expected, such as house collapse (flood flow).

The round marks indicate the location of the school, municipal offices, etc. and public gathering facilities.

Looking at this map, you can easily see where the water will be flooded when a tsunami arrives or when a river overflows due to heavy rain. You can see this and think about whether to evacuate.

The locations of schools, municipal offices, etc. and public gathering facilities are shown because there is a high possibility that they will become evacuation sites in the event of a disaster. If you look at this map, you will find the location of your evacuation. You can also go to evacuation sites that are not affected by flooding.

Source

国土数値情報ダウンロードサイト

https://nlftp.mlit.go.jp/ksj/index.html　（Accessed April 28, 2025）

Site used to create the map

https://kepler.gl/

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This map provides earthquakes that happened around the world from 04/27/2024 to 04/27/2025 and each circle shows where earthquakes occurred.

The size of circles represents how strong the earthquake was. The color of circles shows how deep earthquakes were under the ground.

From this map, you can see most earthquakes happened around Asia and Oceania. Also, some parts of North and South America had strong earthquakes.

Source : https://earthquake.usgs.gov/earthquakes/search/

Site I used for creating map : https://kepler.gl/demo

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The maps we have created display the population density of Turkey with the location and magnitude of earthquakes over the last 3 years overlaid on top. These maps combine data from three different sources to illustrate Turkey’s population density, earthquake fault lines, and recent seismic events. Turkey suffered many casualties and large amounts of damage from recent earthquakes, including the 7.8 magnitude earthquake in February 2023 and the recent 6.2 magnitude Istanbul earthquake.

By integrating data on population densities, past earthquakes, and active fault lines, these maps can be used to highlight high-risk population centers for future earthquakes. This information can help pinpoint specific cities, shopping centers, and residential areas that require better public preparedness, stronger infrastructure, and increased investment to reduce damage and casualties in future earthquakes.

Sources

Global Earthquake Model (GEM) Foundation 2025, GEM Global Active Faults Database, viewed 23 April 2025, https://github.com/GEMScienceTools/gem-global-active-faults.

Humanitarian Data Exchange (HDX) 2025, WorldPop population density for Turkey, viewed 23 April 2025, https://data.humdata.org/dataset/worldpop-population-density-for-turkey.

United States Geological Survey (USGS) 2025, Earthquake Hazards Program, viewed 23 April 2025,https://www.usgs.gov/programs/earthquake-hazards.

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Flood Risk Zones and Infrastructure in Afghanistan 2025

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Flood Risk Zones and Infrastructure in Afghanistan 2025

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Afghanistan is highly vulnerable to flooding, with estimates suggesting that up to 36% of its population faces a high level of flood risk. The 2024 floods in northern Afghanistan resulted in the deaths of over 200 people, and the destruction of over 3,000 homes.

In the heavy floods that struck this February, the country suffered significant impacts, including 36 deaths (including several children), 40 injuries, damage or destruction to 300 homes, and harm to hundreds of acres of farmland.

Our map is designed to assist international humanitarian aid organizations by highlighting the areas and populations most at risk during severe floods. It also displays the locations of healthcare facilities and airports to support the coordination of medical aid and supply deliveries. In the event of a crisis, this information would be critical for delivering timely and effective assistance.

We primarily used Kepler and The Humanitarian Data Exchange website to source our data.

Sources: https://kabulnow.com/2025/02/death-toll-from-recent-rainstorms-and-floods-in-afghanistan-rises-to-36/

https://data.humdata.org/dataset/afghanistan-healthsites

https://data.humdata.org/dataset/ourairports-afg

https://www.vaticannews.va/en/world/news/2025-02/several-children-among-those-dead-in-afghanistan-floods.html

https://datareportal.com/digital-in-afghanistan?utm_source=DataReportal&utm_medium=Country_Article_Hyperlink&utm_campaign=Digital_2025&utm_term=Afghanistan&utm_content=Country_Page_Link

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Wajima City Disaster Prevention Map – Records of Damage and Evacuation

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Wajima City Disaster Prevention Map – Records of Damage and Evacuation

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Project Title: Wajima City Disaster Prevention Map – Records of Damage and Evacuation

Explanation of Project:

We utilized Re:Earth, a WebGIS tool, to create a crisis map and storytelling visualization based on the major earthquake (Magnitude 7.6) that struck the Noto region of Ishikawa Prefecture at 16:10 on January 1, 2024.

Our project visualizes the earthquake’s overview, damage situation, and the locations of evacuation shelters in Wajima City, which suffered the most severe impact.Feel free to try it out and experience it yourself!💻🌏

Data Visualized on the Map🗺️:

1. Slope failure and sediment distribution map

2. Epicenter and seismic intensity at various locations

3. Photos of building collapses and landslide damage

4. Evacuation shelter data for Wajima City

🔗Project Link:https://baejfefggc.reearth.io

👫Team name and our information:

Our teammates are Yukari,Rito,Moe,Yuka,Rensei! We are Aoyama Gakuin University students!

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Population displacement as a consequence of the Sudanese Civil War

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Population displacement as a consequence of the Sudanese Civil War

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This interactive map try to provide insight into the forced displacement of civilians resulting from the Sudanese civil war.

By "default", it plots all the major incidents recorded since 2023 (the start of the current conflict) using ACLED data. “Strategic developments” are hidden but can be toggled on if you want to see them. Simultaneously, countries are shaded according to the number of Sudanese refugees they have taken in, based on UNHCR statistics.

You can also switch to view each country’s Human Development Index, drawn from World Bank data, which allows you to compare living standards in Sudan, in host nations, and in other states involved in the war. Finally, a layer showing Sudan’s mineral resources—an important factor in the conflict—is also available.

Sources :

Notorious events since 2023 : ACLED - Data on Africa https://acleddata.com/data-export-tool/

Refugee Reception : UNHCR - Refugee Data Finder http://unhcr.org/refugee-statistics/download?data_finder%5BdataGroup%5D=displacement&data_finder%5Bdataset%5D=population&data_finder%5BdisplayType%5D=totals&data-finder=on&data_finder%5BpopulationType%5D%5B%5D=REF&data_finder%5BpopulationType%5D%5B%5D=ASY&data_finder%5BpopulationType%5D%5B%5D=IDP&data_finder%5BpopulationType%5D%5B%5D=OIP&data_finder%5BpopulationType%5D%5B%5D=STA&data_finder%5Byear__filterType%5D=custom&data_finder%5Byear__custom%5D%5B%5D=2024&data_finder%5Byear__custom%5D%5B%5D=2023&data_finder%5Bcoo__displayType%5D=custom&data_finder%5Bcoo__country%5D%5B%5D=177&data_finder%5Bcoa__displayType%5D=all&data_finder%5Byear__%5D=&data_finder%5Bcoo__%5D=&data_finder%5Bcoa__%5D=&data_finder%5Badvanced__%5D=&data_finder%5Bsubmit%5D=

Mineral ressources : United States Geological Survey - Mineral Resources Data System (MRDS) https://mrdata.usgs.gov/mrds/

HDI : Human Development Reports - Human Development Index https://hdr.undp.org/data-center/human-development-index#/indicies/HDI

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Population displacement as a consequence of the Sudanese Civil War – Felt

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Our map displays the acute food insecurity situation in Haiti for the projected period of March to June 2024 in the context of the persistent gang war. Using Kepler.gl, we visualized the severity of food insecurity across the country based on IPC phases, with each region color-coded according to the dominant phase: Phase 1 (Minimal), Phase 2 (Stressed), Phase 3 (Crisis), and Phase 4 (Emergency). The darkest areas represent regions facing the most critical conditions. In addition to the color coding, circular layers indicate the estimated population affected, highlighting the regions where the crisis impacts the largest number of people. The map shows that central, metropolitan, and southern regions are particularly affected, with many zones in Crisis (Phase 3) and several in Emergency (Phase 4). This allows us to consider the widespread nature of food insecurity in Haiti and how to prioritize humanitarian interventions by identifying areas where urgent aid and large-scale support are most needed.

Link to the map : https://kepler.gl/demo/map?mapUrl=https://dl.dropboxusercontent.com/scl/fi/8arecda0lnnrfquuatk4a/keplergl_ms3ux4.json?rlkey=k3w6mnrrsdebwj0aw4qoc931y&dl=0

# data[].attributes.custom_properties.Jorl4-dw
Valentine Lahalle ; Isabel Casademunt Triay

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Redlist Alive: Visualizing Threatened Species Across South America

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Redlist Alive: Visualizing Threatened Species Across South America

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Our interactive Kepler.gl map presents South America's current levels of threatened biodiversity. You can click on any dot which represents a species and its corresponding vulnerability level using IUCN Red List data, where we visualized species from Vulnerable to Critically Endangered with intuitive color-coding: Indigo (all species), Yellow (Vulnerable), Orange (Endangered), and Red (Critically Endangered). Our map aims to drive interest in what endangerment looks like, and where it is in a comprehensive, visual way. By mapping biodiversity loss, we give endangered species a voice and a presence. A simple map can reveal great environmental injustices, and no life should disappear unnoticed.

🟦Blue - All included Red List species

🟨Yellow - Vulnerable species (e.g., Hypopygus varii)

🟧Orange - Endangered species (e.g., Sternarchorhynchus mendesi)

🟥Red - Critically Endangered species (e.g., Megadontognathus cuyuniensis)

Link to data:

https://www.iucnredlist.org/search?query=south%20america&searchType=species

Link to Kepler Map: https://kepler.gl/demo/map?mapUrl=https://dl.dropboxusercontent.com/scl/fi/4bz3laxjgad8u3aj65yp8/keplergl_bf08guq.json?rlkey=jq5dkwjlay260711jwfdqxe62&dl=0

Built using Kepler.gl for spatial visualization & data sourced from the IUCN Red List.

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JP Tyler Lee-Iwasawa, Eloise Anquet, Aurora Brendtke

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My name is Kaede Ezaki. I'm a high school student in Niseko. I'm really looking forward to it because I'm interested in creating maps. Let's meet again on the day, everyone!

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Targeting intervention: addressing regional healthcare disparities in Indonesia during the covid-19 pandemic

# data[].attributes.subject
Targeting intervention: addressing regional healthcare disparities in Indonesia during the covid-19 pandemic

# data[].attributes.body
Indonesia is the world's fourth-most-populous country, however has very few hospitals and is lagging behind key healthcare indicators. The lack of healthcare facilities in peripheral, yet populated, islands caused overcrowding of hospitals in Java.

There are 3 main variables in this map:

• number of healthcare facilities shown in a heat map

• population by province

• number of deaths by province

The aim is to show whether there is an under provision of healthcare, comparing facilities, population and # deaths. I believe this map is a preliminary analysis and supports the thesis that it is important to send medical support to specific areas. The map also shows the areas where healthcare investment is most needed for future crises.

The map: https://felt.com/map/Indonesia-lrdQ16GEQduL9Cw9BgtZVl7D?loc=-0.723,109.871,6z

The infographics with my sources: https://www.canva.com/design/DAGl5GUxDpg/Bt7wcXQNynlV59CulSxfOQ/view?utm_content=DAGl5GUxDpg&utm_campaign=designshare&utm_medium=link2&utm_source=uniquelinks&utlId=had8ff714bc

# data[].attributes.body
My name is Kaede Ezaki. I'm a high school student in Hokkaido. I'm interested in making maps, so I want to do my best

# data[].attributes.headline
Displacement of population in East-DRC caused by armed conflicts on natural resources

# data[].attributes.subject
Displacement of population in East-DRC caused by armed conflicts on natural resources

# data[].attributes.body
https://felt.com/map/Displacement-of-population-in-East-DRC-caused-by-armed-conflicts-on-natural-resources-pY9C0QDJXTJq2trnOWIS7gC?loc=-2.046,29.112,7.08z

Since gaining independence in 1960, the Democratic Republic of the Congo (DRC) has been a country marked by political instability and a weak state. Current geopolitical demand for resources fuels internal power struggles. The country is beholden to a ‘resource curse’, meaning that the extraction of natural resources and the associated economic opportunities have a strong negative impact on the country's economic situation, political stability and the quality of life of the population. The presence of metals and rare earths which are in high demand on the world market, such as gold, tourmaline, cassiterite, coltan, and wolfram, fuel the country's internal conflicts between various armed groups who are supported by the DRC, Burundi or Rwanda. The conflicts have led to a humanitarian crisis in the east of the country, particularly in the province of Kivu. There have been numerous massacres of civilians and cases of sexual violence and torture. To escape this violence, the Congolese have fled from their homes to other parts of the country or across borders to neighboring countries. This crisis is exacerbating the already-grave socio-economic inequalities in the country. With the DRC considered one of the poorest countries in the world, population displacement and ongoing conflict are further undermining access to food, healthcare and education.

The map illustrates the population movements in the east of the country caused by the current conflicts between armed groups. The geographical representation highlights the overlap between the location of metal and rare earth mines and the presence of armed groups. These regions are therefore the scene of the largest population movements in the country.

Sources

United Nations, « UNHCR / UGANDA CONGOLESE REFUGEES », 08/04/2025, https://media.un.org/unifeed/en/asset/d335/d3359025, visited on 28/04/2025

UNHCR website on DRC

IOM, « Baseline Assessment - Countrywide Displacement Overview 2024 – Round 2 », 2024, https://dtm.iom.int/datasets/baseline-assessment-countrywide-displacement-overview-2024-round-2, visited on 28/04/2025

IPIS, « Analyse de la carte interactive des zones d’exploitation minière artisanale dans l’est de la République démocratique du Congo », 2023, https://ipisresearch.be/fr/publication/analyse-de-la-carte-interactive-des-zones-dexploitation-miniere-artisanale-dans-lest-de-la-republique-democratique-du-congo-mise-a-jour-2023/, visited on 28/04/2025

# data[].attributes.custom_properties.Jorl4-dw
Auriane GOURANTON, Domitille GUERARD, Malina JOUANNEAU

# data[].attributes.attachment_link.readable_text
Leading a new movement in GIS and map-making

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Turn your QGIS project into an interactive web map with our QGIS plugin, giving your map superpowers like live collaboration and commenting for anyone with a browser. Felt and QGIS go together great, and we are proud to be a flagship sponsor.

Make maps part of your day-to-day operations

Maps are as important as spreadsheets and documents, and should be just as simple to work with. With Felt, your whole team can collaborate spatially without installing software, and it's easy enough for anyone to use.

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Felt creates a beautiful, interactive legend for any data you add.

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View your data as you work with it on the map.

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Start with a map that looks beautiful right away, then customize.

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"My favorite maps have always been the ones made in the moment, either out of necessity or on a whim."

- Tim wallace, senior editor of geography, new york times

"My favorite maps have always been the ones made in the moment, either out of necessity or on a whim."

- Tim wallace, senior editor of geography, new york times

Start creating maps today.

# data[].attributes.attachment_link.title
Felt - The best way to work with maps on the internet

# data[].attributes.attachment_link.description
Felt lets you create maps collaboratively, using world-class data, and share them in a single click. For team projects or epic adventure with friends.

# data[].attributes.body
Hello I'm Chiara! I am Italian and currently I am an exchange student at Hitotsubashi!

# data[].attributes.headline
Fukushima power plant accident and its influence

# data[].attributes.subject
Fukushima power plant accident and its influence

# data[].attributes.body
This map indicates 2 datasets.

1. the density of radiation detected in different areas in Fukushima in 2011. Indicated by the radius of the dots.

2. the public's attentiveness towards environmental radiation pollution in each regional district. Indicated by the depth of purple.

The Great East Japan Earthquake (2011) is notorious for its devastating tsunamis and the Fukushima nuclear power plant incident.

We aimed to display the severity of the radiation pollution from the epicenter of the incident.(eastern side of Fukushima)

Another goal was to showcase how the people's attention to radiation pollution. We then aimed to find any correlation between the two. That is, "the higher the radiation detected, the more attention towards radiation pollution"

After overlaying the two datasets, we can see that there is some correlation.

However, there are shortcomings in this map. One of them is using data from regional districts, and not from individual cities. This means the data of cities that did not follow the trend could have been overwritten by the majority in a regional district. Thus, overgeneralizing the people’s opinion.

Another is the lack of data near the power plant. Although it is obvious that the level of radiation there was life-threatening, it could have given a more holistic view on the event and trend from the issue.

The website we used was Kepler, and extracted data from the following links.

Fukushima Prefecture Annual Survey 2012

Fukushima Prefecture Regional District Data

Fukushima Radiation Detection Data 2011

# data[].attributes.headline
Black Sea Grain Initiative (BSGI) Voyages - War in Ukraine

# data[].attributes.subject
Black Sea Grain Initiative (BSGI) Voyages - War in Ukraine

# data[].attributes.body
Between July 2022 and July 2023, the Black Sea Grain Initiative (BSGI) delivered almost 33 million (metric) tonnes of grain from the Ukrainian ports of Odesa, Chornomorsk, and Yuzhny/Pivdennyi, through their inspection point in Istanbul, Türkiye to ports of almost 50 countries around the world. The deal was a way of ensuring that Ukraine, one of the breadbaskets of the world, could safely transport its grain through the Bosporus.

In July of 2023, Russia announced its decision to leave the BSGI. In previous months, exports had already decreased by 29% in March and 66% in May.

The BSGI was one of many mediation efforts between Russia and Ukraine since Russia invaded in February of 2022. Fighting has now raged for more than three years, and is considered to be the deadliest conflict in Europe since World War II.

The first map depicts the total amount of grain (in tonnes) from Ukranian ports to the each country, represented by their capitals. The second map depicts each voyage's shipment (in tonnes).

The raw data file can be downloaded via the link below:

Joint Coordination Centre. (2023, September 24). Black Sea Grain Initiative Vessel Movements - Humanitarian Data Exchange. Data.humdata.org. https://data.humdata.org/dataset/black-sea-grain-initiative-vessel-movements

Maps were made by Ange Qin using QGIS

Sources

European Council. (2023). Ukrainian grain exports explained. Www.consilium.europa.eu. https://www.consilium.europa.eu/en/infographics/ukrainian-grain-exports-explained/

Nations, U. (n.d.). Black Sea Grain Initiative | Vessel Movements. United Nations. https://www.un.org/en/black-sea-grain-initiative/vessel-movements

U.S. Embassy Tbilisi. (2023, August 10). World leaders call on Russia to rejoin Black Sea grain deal. U.S. Embassy in Georgia. https://ge.usembassy.gov/world-leaders-call-on-russia-to-rejoin-black-sea-grain-deal/

# data[].attributes.headline
From Crisis to Action: Mapping Climate Disaster & Policy (Geo-visual Narrative)

# data[].attributes.subject
From Crisis to Action: Mapping Climate Disaster & Policy (Geo-visual Narrative)

# data[].attributes.body
https://mariellajorge.wixsite.com/fromcrisis2actionmap

(I made a geo-visual narrative in a wix website instead of Esri's StoryMaps since the sharing settings can be finnicky with StoryMaps)

This geo-visual narrative shows climatological disasters all over the world and the global efforts to respond to climate change (e.g., the Paris Agreement). The project looks at data from this century: 2000-2024 (2024 was the most recent available data).

I've created 4 maps as part of the larger narrative: a time series of climate disaster events, impact over time (shown here in this post), international agreements, & an interactive web map to explore agreements by country.

Data Sources: EM-DAT & The World Factbook

# data[].attributes.headline
Mapping Climate Disasters & Policy (Story Map website)

# data[].attributes.subject
Mapping Climate Disasters & Policy (Story Map website)

# data[].attributes.body
https://mariellajorge.wixsite.com/crisistoaction

This storymap shows the efforts countries are taking to respond to climate change.

There are 4 maps.

Data Sources: EM-DAT & The World Factbook

# data[].attributes.headline
Wildfire Evacuation and Transportation Access in California

# data[].attributes.subject
Wildfire Evacuation and Transportation Access in California

# data[].attributes.body
This map examines wildfires greater or equal to 5000 Acres in 2019-2023 and related transportation accessibility during evacuation.

Data Source:

https://gis.data.ca.gov/maps/c3c10388e3b24cec8a954ba10458039d/about

https://www.atsdr.cdc.gov/place-health/php/svi/svi-interactive-map.html

https://gis.data.ca.gov/datasets/900992cc94ab49dbbb906d8f147c2a72_0/explore?location=-41.033146%2C-88.917849%2C9.11

# data[].attributes.headline
ILLEGAL MIGRATION JOURNEY TO ITALY: LIVES FOREVER LOST AMID EU'S INDIFFERENCE

# data[].attributes.subject
ILLEGAL MIGRATION JOURNEY TO ITALY: LIVES FOREVER LOST AMID EU'S INDIFFERENCE

# data[].attributes.body
This map is created to honor the untold journeys of the thousands who risk everything for a chance at safety, dignity, and freedom.

The aim is to shed light on the hidden humanitarian crisis that unfolds across deserts, seas, and borders — where countless lives are lost not just to nature, but to violence, exploitation, and indifference; all happening under the rigorous scrutiny of EU's watch. Where are the human rights in the Union's surveilled detentions camps? those same rights the EU so bluntly discuss about in the confort of Bruxelles' tall buildings.

Each point of suffering on this map is a call for awareness, compassion, and action.

This map is not only to inform but to inspire the much more needed understanding of the human cost behind every crossing.

# data[].attributes.headline
Russian Attacks on Ukrainian Civilian Infrastructure and Weapon Systems (2022-2025)

# data[].attributes.subject
Russian Attacks on Ukrainian Civilian Infrastructure and Weapon Systems (2022-2025)

# data[].attributes.body
Since the start of Russia’s full-scale invasion of Ukraine in 2022, attacks against critical infrastructure, both military and civilian, have been not only frequent, but steadily increasing. To visualize the magnitude of this, I used data from ACLED, with the goal of analyzing which specific types of infrastructure were and are being hit. In order to do so, I had to clean the dataset from over 20 down to 5, Residential, Health, Education, Energy and “Composite” (used for any attack which targeted more than one category at once, hence why it’s also called “>1 tag”). This visualization takes up the left hand side of the screen: at the same time, however, I created a second hex layer (conveniently called “Hex 2”), which can be turned activated on the right side of the screen: the data contained in it is exactly the same, with the exception that it no longer displays the predominant type of infrastructure hit (an effect of “Mode” in Kepler), but rather focuses on how many different types of infrastructure were hit in each area.

The right hand side, however, is mostly designed to visualize a different dataset, which I got from Kaggle. This documents Russian attacks (from October 2022) specifically by weapon systems used: I generalized all the different weapon systems into a series of broader categories (e.g. Cruise Missiles, Ballistic Missiles, Drones & UAV, etc.) and plotted the data. This was a rather challenging task, as there were no coordinates provided, which means I had to implement my own solution. I approached the problem by grouping all the attacks in a specific oblast to its main city, thus creating clusters around the bigger cities (which now encompass data for both attacks on the city itself as well as the rest of the oblast): moreover, the entry “Ukraine” was extremely common, and I symbolically plotted all of those to the center point of the country.

The map is interactive, controlled by a time filter synced for each layer that allows anyone to either just watch the events, or focus on more specific timeframes.

Map made in Kepler.gl

Data Sources:

1) ACLED (Curated Data > Infrastructure): https://acleddata.com/ukraine-conflict-monitor/#update

2) Kaggle (Massive Missile Attacks on Ukraine): https://www.kaggle.com/datasets/piterfm/massive-missile-attacks-on-ukraine/data

# data[].attributes.attachment_link.title
Large-scale WebGL-powered Geospatial Data Visualization Tool

# data[].attributes.attachment_link.description
Kepler.gl is a powerful web-based geospatial data analysis tool. Built on a high performance rendering engine and designed for large-scale data sets.

# data[].attributes.headline
Damaged buildings and NPOS in Noto Earthquake

# data[].attributes.subject
Damaged buildings and NPOS in Noto Earthquake

# data[].attributes.body
This map shows the number of damaged buildings in Ishikawa Prefecture as a result of Noto Earthquake and the number of non-profit organizations working to aid the victims. The map implies that NPOs actually work in areas where damages are serious. As you can see from the map, Suzu city and Wajima city are prefectures where most NGOs operate. We can interprete this as follows. These regions are greatly affected by the earthquake and following tsunami. However, because those prefectures are far away from other regions in Japan, they cannot get support materials from neighboring regions. That's why they need the support from NPOs. In fact, most NPOS working at these prefectures are for material support.

Sources:

https://xview.bosai.go.jp/view/index.html?appid=41a77b3dcf3846029206b86107877780

# data[].attributes.body
Hello!

My name is Chihiro.

I’m a student at Niseko high school in Hokkaido.

My hobby is playing volleyball.

I’m not good at speaking English.

But,I want to challenge myself a lot.

Nice to meet you!

# data[].attributes.headline
Educational Vulnerabilities in Myanmar: Mapping Earthquakes and Attacks

# data[].attributes.subject
Educational Vulnerabilities in Myanmar: Mapping Earthquakes and Attacks

# data[].attributes.body
Myanmar faces compounding crises - from devastating earthquakes in 2024 and 2025 to ongoing airstrikes amid civil conflict. Since the February 2021 military coup, the country has been gripped by escalating violence. Myanmar is now considered the third-deadliest country in the world (ACLED Conflict Index, 2024).

In this context, education systems are particularly vulnerable, with 31.5% of schools having sustained major damage due to conflict (Centre for Information Resilience, 2024). According to the UNESCO, about 1 in 5 children and young people aged 5 to 17 are out of school.

These different problems have led us to highlight the overlapping threats to education by layering maps of earthquake damage, attacks on schools, and existing school infrastructure. The map reveals where access to education is most at risk, showing how conflict and natural disasters intersect to deepen educational vulnerability. By visualizing these compounded risks, the map underscores the areas where humanitarian response is most needed.

We layered data on earthquakes from 1990 to 2025 to show both historical and recent seismic activity, demonstrating recurring risks over time. We included conflict-related data (attacks and arson attacks on schools) to reveal the direct impact of violence on educational infrastructure. Mapping these datasets together helps identify where educational access is most severely threatened by multiple, overlapping hazards.

Legend

Purple dots - Past earthquakes from 1990-2025, color-coded by magnitude

Blue circles - ranges of the 2025 Earthquake

Greenish-blue dots - Education facilities

Yellow dots - Arson attacks on education facilities

Clusters - Attacks on universities and schools

Sources/Data

https://acleddata.com/conflict-index/

https://www.info-res.org/myanmar-witness/reports/schools-caught-in-the-crossfire/

https://www.unicef.org/press-releases/1-3-children-and-young-people-out-school-countries-affected-war-or-natural-disasters

https://ikcest-drr.data.ac.cn/data/93674

https://data.humdata.org/dataset/myanmar-attacks-on-aid-operations-education-health-and-protection

https://ikcest-drr.data.ac.cn/data/99b53

# data[].attributes.custom_properties.Jorl4-dw
CHEN Luijing, HUBAULT Jules, LANCE Mathilde, MAZY Celia

# data[].attributes.attachment_link.title
Large-scale WebGL-powered Geospatial Data Visualization Tool

# data[].attributes.attachment_link.description
Kepler.gl is a powerful web-based geospatial data analysis tool. Built on a high performance rendering engine and designed for large-scale data sets.

# data[].attributes.headline
2011 East Africa drought's impact on education from the years 

# data[].attributes.subject
2011 East Africa drought's impact on education from the years 

# data[].attributes.body
In the parched landscapes of East Africa in 2011, hope seemed like a distant dream. Drought—the worst in 60 years—had robbed the region not just of crops and livestock, but of something even more vital: children’s futures. Schools, once lively centers of learning, emptied out as families were forced to migrate in search of food and water. Yet amid the hardship, stories of resilience emerged. In Kenya, thanks to emergency support programs, students began returning to school. Teachers worked tirelessly to welcome back displaced children, determined to shield them from the long-term consequences of lost education. Their courage and perseverance inspired me to take a closer look at how drought impacts education across East Africa.

Motivated by their story, I decided to investigate the negative impact of drought on education in Kenya, Somalia, and Ethiopia for boys and girls aged 6 to 23. In the map I created, darker colors indicate areas where school attendance dropped, while lighter and brighter colors represent areas where attendance increased—a powerful testament to resilience. The thumbnail image highlights the impact of the 2013 drought on girls aged 6–8. As you can see, while Somalia was severely affected by the crisis, Ethiopia actually saw a slight increase in school attendance, showcasing once again the extraordinary resilience of communities amid the crisis.

As for using the map, I’ve included multiple indicators for you to explore. If you want to focus on a particular year, simply use the filter (the funnel-shaped icon) to select the year you're interested in.

Here are the links to the map and the resources I used:
MAP:
Explore the Map

RESOURCES/Datasets:
Global Data Lab Education Data

One important caveat:
The estimates you see are based on a simple year-over-year subtraction (e.g., 2011 value minus 2010 value). These are naive estimates and do not account for all factors affecting education attendance during the time frame, such as conflict or migration.

Lastly, but certainly not least, a big shoutout to Yoshida Hiroki, who first suggested that we focus on natural disasters in East Africa! You are honestly one of the best Hirokis I have ever met!

# data[].attributes.body
Hello. I am Wako Minato.
I am a student at Niseko High School.
Let's make a map together!

# data[].attributes.headline
2025 Palisades & Eaton Fire Brightness to Infrastructure Damage

# data[].attributes.subject
2025 Palisades & Eaton Fire Brightness to Infrastructure Damage

# data[].attributes.body
We examined the effects of the first weeks of the 2025 Palisades and Eaton wildfires in California on local infrastructure. By comparing luminosity data gathered from NASA with damage reports from CAL Fire regarding wildfire damage, we found that the areas with higher luminosity did not necessarily correspond to where the most infrastructure was destroyed and/or damaged, possibly due to firefighting efforts in the area. Since the wildfires, the areas highlighted are undergoing reconstruction, with 9,000 properties being cleared of hazardous waste in the first 30 days after the fires were extinguished, 2,300 homes are now clear of debris, 8 out of 8 schools are back to teaching in person and 8 out of 9 water systems have been reactivated (Governor of California). California regularly employs wildfire prevention measures, such as “removal of hazardous dead trees, vegetation clearing, creation of fuel breaks and community defensible spaces, and creation of safer ingress and egress corridors” (California Natural Resources Agency). However, even with these measures, it is difficult to prevent wildfires; as our map depicts, once they start, they can quickly spread out of control.

Sources: NASA, CAL Fire

# data[].attributes.custom_properties.Jorl4-dw
Catherine Cox, Cadence Lee, Saya Mueller, Vicky Kang

# data[].attributes.headline
Worldwide Resilience to Natural Catastrophes by Country in 2019.

# data[].attributes.subject
Worldwide Resilience to Natural Catastrophes by Country in 2019.

# data[].attributes.body
This map offers a detailed and comprehensive visualization of global resilience to a variety of natural disasters, focusing on two primary indicators: the extent of damages inflicted on critical infrastructures and the toll taken on civilian populations. Through the analysis of these two crucial aspects, the map seeks to shed light on the varying capacities of countries and regions to absorb the devastating effects of natural catastrophes.

By examining patterns of infrastructure destruction alongside civilian impacts, it becomes possible to identify areas that are particularly vulnerable, as well as regions that demonstrate notable resilience.

Furthermore, this map serves not only as a static snapshot but also as a dynamic tool for understanding the broader human and material consequences of natural events that took place around the world during the year 2019. It emphasizes the uneven distribution of risk and resilience globally.

This map was made thanks to the CATNAT (Permanent observatory of Natural Catastrophes) database.

Sources : CATNAT Database.

# data[].attributes.headline
HDI and Armed Conflict Deaths by Country in 2023

# data[].attributes.subject
HDI and Armed Conflict Deaths by Country in 2023

# data[].attributes.body
This map was created in R Studio, using two datasets: UN’s “Human Development Index” (HDI) for 2023 and Our World in Data’s “Deaths in armed conflicts based on where they occurred, 2023.” This map is meant to show the potential relationship between human development and conflict. HDI—which measures education, health care, and income levels—points to where development is low or high, and where disparities may exist. The index is on a scale from 0 to 1, with 0 indicating low development and 1 indicating high development. A map of conflict deaths shows where violence occurs, either from interstate, intrastate, extra-systemic, non-state conflicts, or one-sided violence. By combining these two measures, we can investigate questions like “are conflicts more common in low-HDI regions” or “are there exceptions of countries with higher HDI that still have violence”? Analysis may point to whether human development is the foundation for peace.

Data Sources: https://ourworldindata.org/grapher/cumulative-deaths-in-armed-conflicts?tab=map

https://hdr.undp.org/data-center/country-insights#/ranks

# data[].attributes.body
In 2024, Sicily faced a severe water crisis driven by low rainfall, elevated temperatures, and inadequate infrastructure management. In 2023, the region recorded an annual average rainfall of 558 mm, significantly below the historical norm of 750 mm. The reduced precipitation critically impacted dams and reservoirs, resulting in water shortages, rationing measures, and the declaration of a state of emergency.

The map illustrates the trend by showing water storage levels over the past year.

The map was made using Felt.

Link to map:

https://felt.com/map/Level-of-Reservoirs-2024-compared-to-2023-9Bce5Fp9C7RgyJJnfq75ezRB?loc=37.617,13.422,8.03z&share=1

Sources:

https://apnews.com/article/italy-sicily-drought-tourism-climate-change-water-63b272f105b49b78e59d6bd6fa1754c9

https://www.geopop.it/SICILIA-CRISI-IDRICA-SICCITA-RAZIONAMENTO-ACQUA-DATI/

https://www.regione.sicilia.it/sites/default/files/2025-01/Report_siccita%27_2024_def.pdf

https://www.regione.sicilia.it/sites/default/files/2024-06/0__2024.06.01_A_Tabella_volumi_invasi.pdf

# data[].attributes.custom_properties.Jorl4-dw
Alessandro Piazzoni Marinetti, Mike Perko, Timnah Olcha

# data[].attributes.attachment_link.title
Water crisis in Sicily 2024 Piazzoni, Perko, Olcha

# data[].attributes.headline
Drought and Health Facilities in East Africa

# data[].attributes.subject
Drought and Health Facilities in East Africa

# data[].attributes.body
Severe drought has recently occurred in East Africa, severely affecting many people. Some experts say such climate crises have undermined sanitation and increased diseases there. Therefore, I explore the correlation between drought and health facilities in that region.

Red and orange dots represent seasonal rainfall accumulation. The more rainfall there is, the bigger and brighter the dots become. This means smaller dots indicate serious drought. Additionally, pink dots represent locations of the health facilities.

According to the map above, health facilities are crowded in southern dry regions, while eastern dry areas do not have much health facilities. As for the northern areas, there are more health facilities despite experiencing sufficient rainfall. Thus, we cannot find any simple correlation between drought and health facilities. For further research, I need to search for datasets related to conflict, population, and economic conditions.

Data source:

Humanitarian Data Exchange. 2024. East Africa – CHIRPS Seasonal Rainfall Accumulation Anomaly by Pentad. East Africa - CHIRPS Seasonal Rainfall Accumulation Anomaly by Pentad | Humanitarian Dataset | HDX

Humanitarian Data Exchange. 2024. Health Facilities in Sub-Saharan Africa. Health Facilities in Sub-Saharan Africa | Humanitarian Dataset | HDX

# data[].attributes.headline
This is how much the earth has shaken in the past 30 days

# data[].attributes.subject
This is how much the earth has shaken in the past 30 days

# data[].attributes.body
https://kenta-nakatani.github.io/earthquake-map/

This interactive webmap shows markers at the epicenter of earthquakes of magnitude 2 or greater, based on the past 30 days of earthquake data released by the USGS. The color of the marker changes according to the magnitude of the earthquake, with stronger earthquakes standing out in red and purple.

Clicking the “Earthquake Information” button in the upper left corner displays a sliding list of earthquakes, allowing you to check detailed occurrence times and magnitudes. In addition, an on/off switch function for plate boundaries allows visual comparison of the relationship between earthquakes and plates. Furthermore, the earthquake data is updated in real time, so the latest information is always displayed.

I had never seen earthquakes occurring across the globe” - that curiosity is the starting point of this map.

We created this map in the hope that by providing a global overview of earthquakes that occur somewhere on a daily basis, it would trigger even the slightest interest in the activities of the Earth.

Supplementary information

If the earthquake markers do not appear, please reload the map again.

For some reason, there is a plate line in the longitude.

Source

Real-time earthquake information

United States Geological Survey (USGS)

https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/all_month.geojson

Plate Coordinates

kaggle

https://www.kaggle.com/datasets/cwthompson/tectonic-plate-boundaries?resource=download

This map was created using MAPBOX

# data[].attributes.attachment_link.title
Earthquake data visualization - Google Chrome 2025-04-28 20-11-33

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In early 2025, President Donald Trump introduced aggressive trade policies with the objective of reducing the United States’ trade deficit and reshoring manufacturing. As in Figure 1, the United States primarily experienced trade deficits with numerous countries, particularly China, Mexico, and Vietnam. The administration commenced by imposing a 25% tariff on imports from Canada and Mexico. Subsequently, in April, the United States announced a new set of reciprocal tariffs affecting a wide range of countries, as illustrated in Figure 2. These tariffs were structured initially based on a combination of existing tariffs and other trade barriers on those specific country.  However, the implementation of the reciprocal tariff order was postponed for 90 days to facilitate negotiations (Figure 3), with the exception of tariffs on Canada, Mexico, and China, where China received the highest tariff at 145%.

Data Source:

the Bureau of Economic Analysis (BEA):

https://www.bea.gov/data/intl-trade-investment/international-trade-goods-and-services

The White House:

https://www.whitehouse.gov/wp-content/uploads/2025/04/Annex-I.pdf

https://www.whitehouse.gov/presidential-actions/2025/04/modifying-reciprocal-tariff-rates-to-reflect-trading-partner-retaliation-and-alignment/

Map created with flourish.studio

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Effects of conflicts in Sierra Leone and difficulty of aid

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Effects of conflicts in Sierra Leone and difficulty of aid

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The Sierra Leone Civil War, which lasted from 1991 to 2001, was a devastating conflict. The root of the war apparently lay in decades of discontent with corrupt government after independence from Britain in 1961 and conflicts over the vast diamond resources.

The war left Sierra Leone's economy and infrastructure severely damaged, highlighting the profound impact of civil conflicts driven by both political and economic factors.

The red points show the conflicts which happen in Sierra Leone, and the dark red circle means more conflicts happen compared with other area. On the other hand, the green points show violence against peacekeepers deployed to conflict-affected countries in sub-Saharan Africa as part of both UN and non-UN peace operations.

By mapping these two data, it could be clear that this country has social disorder issue even now, and aid organizations from other countries face on the difference to guarantee the safety of correspondent. When it comes to aids to Africa, French army pulled out from Mali last year. It would be important not only for other countries’ organizations but also for  the government to stabilize political situation and social order.

Data Sources:

UCDP Peacemakers at Risk (PAR) Dataset version 1.0-2016

https://ucdp.uu.se/downloads/index.html#battlerelated

Sierra Leone - Data on Conflict Events

https://data.humdata.org/dataset/ucdp-data-for-sierra-leone

kepler

file:///C:/Users/%E4%B8%AD%E5%B1%B1%E3%80%80%E5%85%AC%E5%A4%AA%E9%83%8E/Downloads/kepler.gl.html

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On March 11, 2011 off the Pacific Coast of Tohoku Earthquake occurred. The earthquake triggered the greatest tsunami ever recorded in Japan and caused tremendous and infinite damage. Approximately 20,000 people lost their lives, and over 2,500 are still officially reported as missing, while a further 6,000 suffered injuries. In the great Kanto earthquake (1923) and the great Hanshin earthquake (1995), the main cause of death was fire, so the Japanese government and authorities have been devoting resources and time to fire prevention. However, in 2011, what killed most of the people was the tsunami. According to the study by Tohoku University, the victims found at sea were particularly numerous off the coast of the ria coastline, where bays and capes are intricately shaped. In the Rias coastline, tsunamis penetrated deep into the valleys, and as they receded, they slid down the slopes, creating extremely strong backwash currents that swept away everything, including reinforced concrete buildings. A research group examined the 568 victims found at sea by coastal municipalities. As a result, municipalities in Sanriku with Ria coastlines stood out. On the other hand, in areas with plains along the coastline, the number of victims remained low.

This map shows the height of the tsunami and the number of victims found at sea. You can see the difference between the Rias coast and the plains along the coastline.

Data sources:

リアス海岸での津波 - 防災リテラシー研究所

東日本大震災／津波被災地域の町・大字別死亡者分布|谷謙二研究室|（埼玉大学教育学部人文地理学）

図録▽東日本大震災で確認された津波の高さ

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国際関係特別講義　地図課題

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These data illustrate the number of conflicts in the Palestine region. According to the global conflict index at the upper left, Palestine is ranked as the deadliest, the most dangerous and the most diffuse conflict, which means this area is exposed to the danger. In addition, as you can tell, about 4630k people, 88% of total population, are exposed to the violence events. Even though the number of monthly political violence events decreased through six months, it is still a lot compared to other regions.

Data source: ACLED

https://acleddata.com/2024/08/05/palestine-mid-year-metrics-2024/

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In 2022, Pakistan experienced one of the most disastrous floodings in its history. The main cause is heavy monsoon rainfall and melting mountain glaciers by global warming. Due to weak infrastructure like house, bridge, and drainage system, it is estimated that 1739 people were killed and 20 million people are affected. People suffered from starvation, contaminated water, and diseases.

This map shows how much water flooded in Pakistan in 2022 by km² and the number of casualties by city. Basically, blue are is the most serious floods area and the bar shows casualties.

By this visualization, we can understand which city needs better infrastructure and decide priority based on this map.

Data Sources:

UNOSAT. (n.d.). https://unosat.org/products/3346

Irfan, A. (2022, September 16). Pakistan’s flood disaster shows the perils of climate shortsightedness. Foreign Policy. https://foreignpolicy.com/2022/09/14/pakistan-floods-disaster-relief-climate-change/

National Disaster Management Authority (NDMA). (2022, June 1). National Disaster Management Authority (NDMA). https://www.ndma.gov.pk/sitreps/search?start_date=2022-06-01&end_date=2022-10-31

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WHEN THE POWER FROZE: Texas’ Water and Power Crisis in the Wake of Winter Storm Uri

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WHEN THE POWER FROZE: Texas’ Water and Power Crisis in the Wake of Winter Storm Uri

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Map also available on ArcGIS Online

In February of 2021, the United States was struck by a severe winter storm. Dubbed Winter Storm Uri by the Weather Channel, it became the deadliest storm since the 1993 Storm of the Century. Uri brought freezing temperatures and snowfall to all of Texas, causing massive power failures across the state. These power failures underscored the Electric Reliability Council of Texas' (ERCOT) failure to effectively winterize the state's energy infrastructure, which led to hundreds of deaths.

Existing maps of Winter Storm Uri's infrastructure impacts rely on counties as areal units, leading to undervisualization of the storm's true population impacts. This map instead visualizes the total populations in Texas directly impacted by the storm. Energy infrastructure was mapped using archived power outage data during the storm and geocoded to each power provider's approximate service area. Failures in water treatment infrastructure were recorded by combining data on boil-water advisories with public water service (PWS) regions.

This map strongly emphasizes the populations impacted by Winter Storm Uri, shedding new light onto the storm's aftermath.

Map created with ArcGIS Pro

Map also uploaded for public view on ArcGIS Online

Data Sources

Poweroutage.us. (2021). Texas Power Outages. Archived by Wayback Machine. https://web.archive.org/web/20210216052935/https://poweroutage.us/area/state/texas

Regions. (2025). Texas Division of Emergency Management. Texas.gov. https://tdem.texas.gov/regions

Texas Commission of Environmental Quality. (2021). Public Water Systems with Lifted Boil Water Notices 3-22-2021. https://www.tceq.texas.gov/downloads/response/temporary-suspension-of-rules-due-to-severe-weather/boil-water-recension-list.pdf/view

Texas Water Development Board. (2025). Water Service Boundary Viewer. https://www3.twdb.texas.gov/apps/WaterServiceBoundaries/Home/Overview

Additional Reference

Winter Storm Uri’s Impacts & Pathways to Resilience in Texas - Houston Advanced Research Center. (2025, January 30). Houston Advanced Research Center. https://harcresearch.org/research/uri/

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hi guys , lets map the Global

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Passion for mapping

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This map illustrates major chemical spill disaster that occurred across the United States between 2017 and 2024. Each marker represents a distinct incident, providing key information including the name of the incident, its location, the time it occurred, and the type of chemical spill involved. The markers are color-coded to indicate the year of occurrence, allowing viewers to track temporal trends and patterns in hazardous material incidents. The color coding is as follows:

• 2025 — Light Blue

• 2024 — Green

• 2023 — Orange

• 2021 — Purple

• 2020 — Dark Brown

• 2019 — Cyan

• 2018 — Light Brown

• 2017 — Light Purple

The map highlights both high-profile accidents and lesser-known regional incidents, showing how widespread and frequent such disaster have become. Notable cases such as the Deer Park chemical fire in Texas (2019) are among the mapped incidents. This visualization aims to raise awareness about chemical safety and emergency preparedness across the U.S and the world.

Here is the link of the map and resources:

https://earth.google.com/earth/d/1l1rzclxEG_Gtg92LYkU40q433fyGhkh0?usp=sharing

https://www.csb.gov

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University of the Philippines Diliman, Roxas Ave, Diliman, Quezon City, Metro Manila

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SHAKING THE ARCHIPELAGO: 25 years of Seismic Activities in the Philippines

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SHAKING THE ARCHIPELAGO: 25 years of Seismic Activities in the Philippines

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The Philippines is one of the most disaster-prone countries in the world. Situated along the Pacific Ring of Fire, it experiences frequent seismic activity due to active tectonic movements, particularly along the Philippine Trench and other fault systems. According to the 2024 World Risk Report by UNICEF, the Philippines ranks first among countries with the highest disaster risk.

This map visualizes 25 years of earthquake occurrences (2000–2024), highlighting seismic hotspots and the country's vulnerability to tectonic hazards. The total recorded earthquakes, as well as earthquake-related deaths, injuries, affected populations, and economic damages (in USD) sourced from USGS EHP and EM-DAT.

Map made with QGIS.

Map by Ariel Dome

Data Sources:

OpenTopography. (2024). Digital Elevation Model (DEM) via OpenTopography Plugin in QGIS. Retrieved from https://opentopography.org

Centre for Research on the Epidemiology of Disasters (CRED). (2024). EM-DAT: The International Disaster Database [Data file]. Université Catholique de Louvain. Retrieved from https://www.emdat.be

U.S. Geological Survey. (2000–2024). Earthquake Hazards Program data and reports. Earthquake Hazards Program.

GADM. (2022). GADM database of global administrative areas, version 4.1 [Shapefile]. Retrieved from https://gadm.org

Dome, A. (2025). Philippine Fault Lines and Trenches [Manual Digitized Geospatial dataset]. Geoportal Philippines. Retrieved from https://geoportal.gov.ph

Bernardo, T. I., Bajarias, T. G. (n.d.). Philippine Fault Lines and Trenches [Manual Digitized Geospatial dataset].

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Ariel Dome, UPRI Youthmappers - UP Diliman

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Shaking the Archipelago 25 years of Seismic Activities in the Philippines

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Hello! Greetings from UP Resilience Institute YouthMappers from the Philippines. We are excited to be participating in this Mapathon ;)

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The number of collapsed buildings and the extent of damage in areas where national roads are concentrated in Turkey-Syria earthquake

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The number of collapsed buildings and the extent of damage in areas where national roads are concentrated in Turkey-Syria earthquake

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I have extracted data on the number of buildings that collapsed in various locations during the Turkey-Syria earthquake of February 2023, and have color-coded the extent of the collapse on a map. The darker the color, the greater the number of collapsed buildings.

The epicenter of the Turkey-Syria earthquake was located along the Turkey-Syria border. I also added national roads to the map filter to show the correlation between areas along roads and the number of buildings destroyed.As a result, even though they are far from the epicenter, the areas where the D400 and D815 intersect and where the D400, D835, and D850 intersect are darker in color due to dense national roads, indicating that the number of collapsed buildings is high.

This map shows that even if the epicenter is far away, the number of collapsed buildings and damage will be greater in urban areas where infrastructure is concentrated, providing data for us attending universities in urban areas to prepare for future earthquakes.

Data sources:

[1]HOTOSM Turkey Destroyed Buildings (OpenStreetMap Export) | Humanitarian Dataset | HDX

Reference:

(PDF) Earthquakes, Existing Buildings and Seismic Design Codes in Turkey

Map created in kepler.gl

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千葉県柏市光ケ丘２丁目１−１ 麗澤大学

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I’m Yuzuka Fukai.

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Hello! My name is Haru!

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国際学部国際学科IEC専攻2年矢口拓弥

よろしくお願いします！

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My name is Aoi Miyatake.

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千葉県柏市光ケ丘２丁目１−１ 麗澤大学

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I'mHaruna

This photo shows flowers in front of the Satsuki campus of Reitaku University.

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小竹しずく
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Mika

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千葉県柏市光ケ丘２丁目１−１ 麗澤大学

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Hello‼︎ my name is Iori Kokubo!

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Hello! My name is Moriumi.

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なかしゅうですこんにちわ

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なかしゅうですこんにちわ

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