# data[].attributes.body
Storymap about the 3 disasters that took the most lives.

# data[].attributes.attachment_caption
Caution!　The heatmap is not displayed at the loading stage; toggle hide/unhide and display it again.

# data[].attributes.headline
Best Narrative: Disasters and Earthquake 

# data[].attributes.subject
Best Narrative: Disasters and Earthquake 

# data[].attributes.body
I have extracted earthquakes from all disasters and displayed them in 3D.

The flat, green heat map represents all disasters.

The color of the 3D Hex represents the intensity of the earthquake, and the height represents the density.

From this map we can see that,

1. earthquakes account for the majority of disasters

2. that earthquakes are dense.

3. there are places where there are no earthquakes at all.

Finally, I want to find out what is happening in the areas where the green is darker (other disasters).

# 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.attachment_caption
Historically, how might Wealth Inequality and Deaths by Floods & Earthquakes intersect? (1901-2024) – created on Felt

# data[].attributes.headline
Best Design: Dangerous waves on water and land, yet we persevered.

# data[].attributes.subject
Best Design: Dangerous waves on water and land, yet we persevered.

# data[].attributes.body
This map helps illustrate the historical intersection of where some of our most disadvantaged global community members live, and how over this last century, the places where they live have been the most impacted by Earthquakes and Floods.

I pulled World Bank subnational data on the Gini index to determine Wealth Inequity as a means to see where disadvantaged people live. Dark green is where the most inequality exists, while lighter yellows are where the WB reports more wealth equity. Some places are white, because no data exists (perhaps for political reasons the nations opted out).

To expand this project, I would consider adding photos to the pink hotspots to show how these bodies of water look like. Or I might overlay newspaper headlines, audio or video materials for each region to provide more historical context for some of the larger natural disasters. Or at least give visibility to some of the folks affected by floods and earthquakes.

Given more time, or using another platform, I would have also added interactive temporal filters to observe the floods and earthquakes (and the total number of deaths) by decade. This would be an interesting experiment to see if death rates decreased, and where in the world did we increase our survival rate.

Rather than just seeing this map to show social injustices and tragedies we have survived through...I encourage us to view it as a testament to people's resilience over time. Both our ancestors and the global neighbors today.

Please feel free to click to explore the project here: https://felt.com/map/Historically-how-might-Wealth-Inequality-and-Deaths-by-Floods-Earthquakes-intersect-1901-2024-ptxTJgPLTKWRjiZfMYSKnD?loc=15.05,24.58,2.89z&share=1.Thank you!

# data[].attributes.attachment_link.title
Historically, how might Wealth Inequity and Deaths by Floods & Earthquakes intersect? (1901-2024) – Felt

# data[].attributes.attachment_link.description
The best way to make maps on the internet.

# data[].attributes.headline
People's Choice and Best Map: Biological Epidemic & Infestation Map: Total Deaths (1900 - 2023)

# data[].attributes.subject
People's Choice and Best Map: Biological Epidemic & Infestation Map: Total Deaths (1900 - 2023)

# data[].attributes.body
This map illustrates the global impact of biological epidemics and infestations over the past century. This biological disaster has caused the deaths of more than 9.6 million people in the past 100 years.

The map provides a complete view of the global toll of these events by highlighting the percentage of deaths in each region of the world and displaying the color code of each country, indicating the percentage of deaths linked to these events. Moreover, the map also highlights the top 10 countries with the highest mortality rates, providing a stark visual of the most affected areas.

This map is a very powerful tool that conveys the terrible effects of these events worldwide, encouraging us to take action to protect our society and create a safer world to live in.

Data Sources:

[1] EM-DAT, CRED / UCLouvain, Brussels, Belgium

[2] Global International Boundaries - USGSFieldMaps, U.S. Department of State, U.S. Geological Survey

Map created in QGIS

# data[].attributes.custom_properties.Jorl4-dw
Ariel Dome, University of the Philippines - Diliman

# data[].attributes.headline
Best Data Analysis: Disasters displace people

# data[].attributes.subject
Best Data Analysis: Disasters displace people

# data[].attributes.body
I was interested to see how disasters impact homelessness. I used the disaster magnitude and homeless data to create this map. Link to map: Link to Map: https://uhm.maps.arcgis.com/apps/mapviewer/index.html?webmap=3ef2a27acae24ae2af46e9fb266d3ec8

The data of homeless people is based on the people requiring shelter based on number of homes destroyed x 5. As one example (visible in popup data in the map), the large earthquake (7.6) in Pakistan left an estimated 5,000,000 people in need of shelter and over 73,000 people dead. The largest magnitude of disasters were in Asia/South Asia.

# data[].attributes.custom_properties.Jorl4-dw
Jolie Wanger, University of Hawaii at Manoa

# data[].attributes.attachment_caption
Heat Map of Areas Most Susceptible to Earthquake Damage

# data[].attributes.headline
Heat Map of Potential Earthquake Infrastructure Intervention Sites 

# data[].attributes.subject
Heat Map of Potential Earthquake Infrastructure Intervention Sites 

# data[].attributes.body
Overlay of earthquake fault lines, events from UN dataset that were filtered down to earthquakes that had 1000+ deaths and $100k USD in damages

This would be a preliminary map in a study to identify areas that may need interventions in the form of infrastructure improvements and more stringent development regulations to make these cities more resilient to earthquakes.

# data[].attributes.body
Our team focused on the number of people who were affected by the earthquakes in Japan between 2019-2024. The colors and size of the pins changed depending on the extent of the damage.

# data[].attributes.custom_properties.Jorl4-dw
Shiori, Aki, Kentaro, Chisato from Aoyama Gakuin University 

# data[].attributes.attachment_link.title
How Many People were Affected by Earthquakes occurred in Japan (2019~2024)ualization

# data[].attributes.attachment_link.description
Beautiful, easy data visualization and storytelling

# data[].attributes.headline
Transport Disaster Casualties per Country in Africa, 1935 - 2024 

# data[].attributes.subject
Transport Disaster Casualties per Country in Africa, 1935 - 2024 

# data[].attributes.body
Transport disasters are a subtype of technological disasters that encompass events that involve any type of transportation method, such as but are not limited to road collisions, derailments, ship sinkings, and plane crashes.

The EM-DAT Data recorded 6,257 different entries of transport disasters, wherein approximately 32% of these disasters (1,986 events) occurred in Africa alone, with the earliest recorded entry occurring in 1935. The map shows the number of casualties that the continent has recorded since the first recorded event.

What's sad about this is that these disasters could have been easily preventable had people paid attention to the status of their environment, the condition of the transportation method, and their state of mind upon embarking on the said journey. The numbers represent the number of people who failed to make it to their intended destination.

# data[].attributes.custom_properties.Jorl4-dw
Arjan Dave Alfeche, University of the Philippines - Diliman

# data[].attributes.headline
Flood Events in the Twenty-First Century by Year and Magnitude

# data[].attributes.subject
Flood Events in the Twenty-First Century by Year and Magnitude

# data[].attributes.body
This map categorizes flood events across the globe in the twentieth century both by year and magnitude. By doing so, we can explore how the frequency and destructiveness of floods have changed over the years, and begin to hypothesize the reasons for these changes. Even by performing a quick visual analysis of the map, it is apparent that there have been more floods of high magnitudes in recent years, particularly from 2013 to the present. This may be due to global climate change, which has affected weather patterns and sea level worldwide, likely inducing more frequent and greater magnitude flood events.

# data[].attributes.custom_properties.Jorl4-dw
Krissie Essilfie, University of Southern California

# data[].attributes.headline
Oil Spills 1900-2023. Omits most infamous Oil Spill.

# data[].attributes.subject
Oil Spills 1900-2023. Omits most infamous Oil Spill.

# data[].attributes.body
The map I created has 8 data points. The screenshot includes an oil spill in Santa Barbara, CA in 2015, but due to limitations with data set and me being a novice it labeled this in Central America. The most infamous Oil Spill that started the Earth Day movement is missing from the data set. "On January 28, 1969, an oil well off the coast of Santa Barbara, Calif., experienced a blowout. The result was an oil spill that at the time ranked as the largest in U.S. waters." (NPR). The data set for Oil Spills only includes data from 1978. I am curious as to why the 1969 oil spill is missing. More questions than answers. I can only speculate. Link to NPR article https://www.npr.org/2019/01/28/688219307/how-californias-worst-oil-spill-turned-beaches-black-and-the-nation-green Link to Oil Spill map https://felt.com/map/Oil-Spill-ECTVqrliQkOUQ4oriuUQJB?loc=35.26,-96.52,3.3z&share=1

# data[].attributes.body
This map shows every earthquake that has happened around the world from 1924-2024. The magnitude of the earthquakes are indicated by the color of the dot. The yellow dots are the least severe earthquakes and the purple dots are the most severe.

This map was created on Kepler.gl.

# data[].attributes.headline
Correlation between magnitude and number of victims in Asia

# data[].attributes.subject
Correlation between magnitude and number of victims in Asia

# data[].attributes.body
Our group, composed of Japanese students from AGU (Aoyama Gakuin Universiti), mapped earthquakes that occur in the Asian region through analysis. Japan, where we live, is one of the most earthquake-prone countries in the world, due to the interconnections of various continental plates. Since we Japanese encounter earthquakes on a daily basis, we are the most experienced earthquake survivors among the students participating in this event. Therefore, we analyzed and mapped whether there is a change in the number of victims depending on the magnitude of the earthquake and whether there is a correlation between the two data.

# data[].attributes.body
What an amazing dataset. So much to explore. Check out Tableau and look through these disasters and where they are in the world. - https://public.tableau.com/shared/B9G7CDRQW?:display_count=n&:origin=viz_share_link

# data[].attributes.body
The map shows the main earthquakes that have occurred in Mexico since 1900, with two symbols: by magnitude and the radius of the second symbology is associated with the number of deaths.

It can be seen that the earthquake that occurred in 1985 on the coast of the state of Guerrero remains the most devastating to date, with a total of 9,500 deaths.

The issue of earthquakes is an important issue in Mexico, since nearly 20 million people live in the Valley of Mexico area. A large-scale earthquake without a prevention culture could be disastrous.

# data[].attributes.headline
From 1963 to 2023: A 60-Year Visual Chronicle of Global Disasters

# data[].attributes.subject
From 1963 to 2023: A 60-Year Visual Chronicle of Global Disasters

# data[].attributes.body
I initially used Tableau to clean the data and compile the annual total of disasters for each country. I employed the visualization tool Flourish to create a comprehensive graph representing the total disasters and their impacts from 1963 to 2023. The graph vividly illustrates how disasters have become increasingly dispersed and numerous worldwide over these 60 years. Notably, both the frequency of disasters and the death toll associated with them have risen.

The graph tells a compelling story of escalating vulnerability and exposure to natural and man-made calamities globally. As you move through the timeline, you can observe spikes in disaster occurrences and sharper increases in mortality rates, underscoring the growing intensity and reach of these events. This visualization serves as a stark reminder of the urgent need for enhanced disaster preparedness and resilient infrastructure to mitigate the human and economic costs of such devastating incidents.

Subsequently, I created a set of scatter plots to illustrate the relationships over these 60 years between the years, the number of disasters, and the total number of people affected. Each dot represents a country. Generally, most countries do not face many disasters annually, yet they have large affected populations. In terms of widespread impact, China suffers significantly from both large-scale disasters and high disaster counts. Moreover, we can observe that not only are the large bubbles prominent, but the colors denoting higher counts are also predominantly found in Asia, especially in China and occasionally in India as the most affected countries.

# data[].attributes.custom_properties.Jorl4-dw
Mingyue Ding (Katherine) - University of Southern California

# data[].attributes.attachment_link.title
Plots Set - 1963-2023 International Disaster Number

# data[].attributes.attachment_link.description
Beautiful, easy data visualization and storytelling

# data[].attributes.body
I'm Paulo, nice to meet you all.

# data[].attributes.headline
9 million people die every year from hunger

# data[].attributes.subject
9 million people die every year from hunger

# data[].attributes.body
On this map, we can visually analyze the correlation between drought and food insecurity. This correlation arises because countries require land to feed their populations. Drought diminishes water availability and crop yield, causing food prices to soar and making it difficult for low-income individuals to maintain a sufficient and healthy diet. The black and blue colors represent drought occurrences worldwide, with blue indicating more severe events. The yellow circles highlight countries where access to food remains a challenge.

https://www.wfp.org/news/world-wealth-9-million-people-die-every-year-hunger-wfp-chief-tells-food-system-summit#:~:text=In%20world%20of%20wealth%2C%209,System%20Summit%20%7C%20World%20Food%20Programme

# data[].attributes.custom_properties.Jorl4-dw
Paulo Matos - University of Hawai'i at Manoa

# data[].attributes.attachment_caption
Historically, how might Wealth Inequality and Deaths by Floods & Earthquakes intersect? (1901-2024) – created on Felt

# data[].attributes.headline
Dangerous waves on water and land, yet we persevered.

# data[].attributes.subject
Dangerous waves on water and land, yet we persevered.

# data[].attributes.body
This map helps illustrate the historical intersection of where some of our most disadvantaged global community members live, and how over this last century, the places where they live have been the most impacted by Earthquakes and Floods.

I pulled World Bank subnational data on the Gini index to determine Wealth Inequity as a means to see where disadvantaged people live. Dark green is where the most inequality exists, while lighter yellows are where the WB reports more wealth equity. Some places are white, because no data exists (perhaps for political reasons the nations opted out).

To expand this project, I would consider adding photos to the pink hotspots to show how these bodies of water look like. Or I might overlay newspaper headlines, audio or video materials for each region to provide more historical context for some of the larger natural disasters. Or at least give visibility to some of the folks affected by floods and earthquakes.

Given more time, or using another platform, I would have also added interactive temporal filters to observe the floods and earthquakes (and the total number of deaths) by decade. This would be an interesting experiment to see if death rates decreased, and where in the world did we increase our survival rate.

Rather than just seeing this map to show social injustices and tragedies we have survived through...I encourage us to view it as a testament to people's resilience over time. Both our ancestors and the global neighbors today.

Please feel free to click to explore the project here: https://felt.com/map/Historically-how-might-Wealth-Inequality-and-Deaths-by-Floods-Earthquakes-intersect-1901-2024-ptxTJgPLTKWRjiZfMYSKnD?loc=15.05,24.58,2.89z&share=1.Thank you!

# data[].attributes.attachment_link.title
Historically, how might Wealth Inequity and Deaths by Floods & Earthquakes intersect? (1901-2024) – Felt

# data[].attributes.attachment_link.description
The best way to make maps on the internet.

# data[].attributes.body
Hello, this is Mihiro and Mayu team from Aoyama gakuin university. In day2, we will submit about a map of earthquake in Japan. This is a map showing which areas in Japan have a high probability of earthquakes from J-SHIS Map.The darker the color, the higher the probability of an earthquake occurring, and the lighter the color, the lower the probability.

We can see where the probability of an earthquake is high and where the risk is high.According to this map, this will be one of the factors that will determine where you live.

# data[].attributes.headline
Correlation between magnitude and number of victims in Asia

# data[].attributes.subject
Correlation between magnitude and number of victims in Asia

# data[].attributes.body
Our group, composed of Japanese students from AGU (Aoyama Gakuin Universiti), mapped earthquakes that occur in the Asian region through analysis. Japan, where we live, is one of the most earthquake-prone countries in the world, due to the interconnections of various continental plates. Since we Japanese encounter earthquakes on a daily basis, we are the most experienced earthquake survivors among the students participating in this event. Therefore, we analyzed and mapped whether there is a change in the number of victims depending on the magnitude of the earthquake and whether there is a correlation between the two data.

# data[].attributes.body
Red point is the emergency evacuation list for Ishikawa Prefecture and white point is an evacuation center for the Noto Peninsula earthquake. It is clear that the tidal wave from the Noto Peninsula earthquake arrived quickly. So we learned that the emergency evacuation shelters were for people living along the coast.

# data[].attributes.body
In Japan, where we live, a historic earthquake occurred in 2011, resulting in a signficant loss of life. i want everyone to know about this disaster.

# data[].attributes.headline
Taiwan’s Earthquakes: A Comparative Analysis of Past and Present-Mapathon day2

# data[].attributes.subject
Taiwan’s Earthquakes: A Comparative Analysis of Past and Present-Mapathon day2

# data[].attributes.body
Our team has created a presentation using Google Earth Project to document the Taiwan earthquake that occurred on April 3, 2024 (DisNo.2024-0189-TWN). We have focused on the locations that were particularly affected by the earthquake and compared them to the past.

# data[].attributes.attachment_link.description
Aw snap! Google Earth isn't supported on your browser. You may need to update your browser or use a different browser. Please see our system requirements for more information. Chrome is a great choice and you can download it here. Or if you're feeling adventurous, you can try Earth anyway by choosing an option below.

# data[].attributes.body
Yahho-!!! How are you, everyone????

I'm Mifuyu~!

I'm so excited to share your wonderful and creative maps together!!!!

See you iArena!!!!

# data[].attributes.body
This map illustrates the occurrences of (orange) storms and (cyan) floods in various regions of China.

According to the map report, these disasters mostly occur in the southern regions of China, primarily due to several reasons:

Geographical environment: The southern regions of China have complex terrain, numerous rivers, and low-lying areas.

Climate characteristics: The climate in southern China is humid, with high levels of rainfall, leading to rising river levels and increased surface water.

High groundwater level: The groundwater level in the southern regions is generally high, with high soil moisture content. During prolonged rainfall, both surface and groundwater levels rise.

Human activities: Accelerated urbanization and industrialization have led to a decrease in the water retention capacity of land, disruption of natural water systems, and inadequate urban drainage systems.

Therefore, the combination of these factors contributes to the higher frequency of storms and floods in southern China.

# data[].attributes.attachment_link.title
無題のプレゼンテーション (2)

# data[].attributes.headline
The most affectedcountry of four disasters.

# data[].attributes.subject
The most affectedcountry of four disasters.

# data[].attributes.body
We have summarized the countries and cities that were most affected, as well as secondary disasters and their impacts.

# data[].attributes.headline
The history of earthquakes with a magnitude of 2.5 or more and earthquakes that occurred in the world(ad. number of death)

# data[].attributes.subject
The history of earthquakes with a magnitude of 2.5 or more and earthquakes that occurred in the world(ad. number of death)

# data[].attributes.body
We made a map that compares the earthquakes that occurred in the world from 1970 to 2014 and how many of them had earthquakes with a magnitude of 2.5 or higher.

Yellow →high magnitude

Red→number of death

# data[].attributes.attachment_caption
Caution!　The heatmap is not displayed at the loading stage; toggle hide/unhide and display it again.

# data[].attributes.body
I have extracted earthquakes from all disasters and displayed them in 3D.

The flat, green heat map represents all disasters.

The color of the 3D Hex represents the intensity of the earthquake, and the height represents the density.

From this map we can see that,

1. earthquakes account for the majority of disasters

2. that earthquakes are dense.

3. there are places where there are no earthquakes at all.

Finally, I want to find out what is happening in the areas where the green is darker (other disasters).

# 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.body
The map displays data on floods (red) and earthquakes (blue) in Russia. From this map, it appears that disasters in Russia are quite rare.

# data[].attributes.attachment_link.title
無題のプレゼンテーション (2)

# data[].attributes.body
This is a simple map of Taiwan's Emergency Shelters. The green dots represent the emergency shelters, and the big red dot represents the epicenter of the 7.2 magnitude earthquake.

# data[].attributes.body
This is World Flood map!!

It has been raining a lot lately,and the rainy season will soon begin in Japan,so this is to raise awareness of disaster preventation.

# 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.body
This map shows the Earthquakes with highest number of fatalities in China.

# data[].attributes.body
1. The picture of plate tectonics

2. The map about earthquakes

3. The map about tsunami

These maps are showing relations among plate tectonics, earthquakes and tsunamis.

Tsunamis happened at boundaries of plates on the ocean.

# data[].attributes.headline
The history of earthquakes with a magnitude of 2.5 or more and earthquakes that occurred in the world

# data[].attributes.subject
The history of earthquakes with a magnitude of 2.5 or more and earthquakes that occurred in the world

# data[].attributes.body
We made a map that compares the earthquakes that occurred in the world from 1970 to 2014 and how many of them had earthquakes with a magnitude of 2.5 or higher.

# data[].attributes.body
Recently there have been many large earthquakes in Japan. Therefore, we have made it easy to see on the map where many earthquakes are occurring.

The darker the color, the more earthquakes are occurring in the vicinity.

# 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
Biological Epidemic & Infestation Map: Total Deaths (1900 - 2023)

# data[].attributes.subject
Biological Epidemic & Infestation Map: Total Deaths (1900 - 2023)

# data[].attributes.body
This map illustrates the global impact of biological epidemics and infestations over the past century. This biological disaster has caused the deaths of more than 9.6 million people in the past 100 years.

The map provides a complete view of the global toll of these events by highlighting the percentage of deaths in each region of the world and displaying the color code of each country, indicating the percentage of deaths linked to these events. Moreover, the map also highlights the top 10 countries with the highest mortality rates, providing a stark visual of the most affected areas.

This map is a very powerful tool that conveys the terrible effects of these events worldwide, encouraging us to take action to protect our society and create a safer world to live in.

Data Sources:

[1] EM-DAT, CRED / UCLouvain, Brussels, Belgium

[2] Global International Boundaries - USGSFieldMaps, U.S. Department of State, U.S. Geological Survey

Map created in QGIS

# data[].attributes.custom_properties.Jorl4-dw
Ariel Dome, University of the Philippines - Diliman

# data[].attributes.body
In this map, the layers land, airports, tectonic plates, and timezones were used in sync with a heat map of disaster events based on their historic status.

# data[].attributes.attachment_link.title
2024 International Humanitarian Mapathon – Felt

# data[].attributes.attachment_link.description
The best way to make maps on the internet.

# data[].attributes.body
We have shown the relationship between earthquakes and tsunamis on a map.

https://docs.google.com/presentation/d/1Nza2tfuBhuw5Pac-8cHdIHYRrfHOfwdxP-t-JKGUTGY/edit?usp=sharing

# data[].attributes.custom_properties.Jorl4-dw
Group members：YeJi Lee, Yuuna Tokita, YoungEun Kim, Aoi Takahashi (Reitaku University)

# data[].attributes.body
This map shows the number of natural disasters occurring in China. Earthquakes, storms, and floods are each shown.Red points indicate where earthquakes occurred, blue points indicate where flooding occurred, and orange points indicate where storms occurred.

# data[].attributes.attachment_link.title
無題のプレゼンテーション (1)

# data[].attributes.body
I'm Koki

Nice to meet you

# data[].attributes.body
this map shows the china　and This describes the location of the earthquake in China

# data[].attributes.body
Hi❣️

# data[].attributes.body
hello

# data[].attributes.body
My name is Misaki. I’m Reitaku University student.

# data[].attributes.body
I’m Honoka at Reitaku University.

# data[].attributes.body
Hello everyone, nice to meet you!

I'm Riria!

from Japan🇯🇵 Reitaku university

I'm very happy to be able to participate with you all🫶

# data[].attributes.body
Nice to meet you!!
I’m Natsuhi Kuramochi.
I'm Reitaku University students!
My hobby is watching football!

# data[].attributes.body
nice to meet you

# data[].attributes.body
Hello all! I'm mikito from ReitakuUniversity. Iwana enjoy this event. Nice to meet you.

# data[].attributes.body
Hi, I’m Honoka;) from Japan!

Nice to meet you

# data[].attributes.body
I want to study English.

# data[].attributes.body
Nice to meet you.My name is Shien.

# data[].attributes.body
I'm Takumi

Nice to meet you

# data[].attributes.body
I’m Asuka Reitaku University second year student.

Nice to meet you 😆

# data[].attributes.body
hello,I’m yuan.I’m from Japan. I’m excited about what I will learn from this event.

Thank you!

# data[].attributes.body
Hello!

we are Riku,Kenta,Shinta,Shion!

Riku is very cool?

Thank you. :)

i’m japaneeeeese!!!!!

# data[].attributes.body
Hi! I’m Ayano from Reitaku University.

I used to live in the U.S. for about 4 years. I’m love going to Cafes, Bars and The ocean.

# data[].attributes.body
Hello. Nice to meet you!

I'm Aoi. I'm a student at Reitaku University. This is my first time making a map, but I'll do my best.

# data[].attributes.body
Hi!My name is Nagisa.Nice to meet you!

# data[].attributes.body
My name is Fuuga Nakamura！

I'm Reitaku University students！

Nice to meet you！！

# data[].attributes.custom_properties.Jorl4-dw
Hello, I am a third year student at Reitaku University in Chiba, Japan. Today I am participating in the Mapathon with my friends. Let's all have fun!

# data[].attributes.body
Reitaku University 3rd grade

Nice to meet you!

# data[].attributes.body
I'm Haruki.

Reitaku University

# data[].attributes.body
Hello! Nice to meet you all!

I'm Aoi at Reitaku University.

I want to enjoy this event:)

# data[].attributes.body
Hi👋

I'm a student at Reitaku University.

I’m Korean and I am studying in Japan.

Nice meet you 👍

# data[].attributes.body
nice to meet you every one

# data[].attributes.body
I'm Mika！ I'm from Reitaku University！

I like Mapathon！

Nice to meet you♡

# data[].attributes.body
Hello👋 I’m Reitaku diversity student!

Nice to meet you 😆

# data[].attributes.body
I’m Maria.

I’m from Reitaku University.

I love k-pop. Nice to meet you:)

# data[].attributes.headline
Hello. I am Wataru. I am 4th grade of Reitaku University. Nice too meet you!

# data[].attributes.subject
Hello. I am Wataru. I am 4th grade of Reitaku University. Nice too meet you!

# data[].attributes.body
Nice meet you everyone!

I’m young eun kim. And I’m junior student in reitaku university!

# data[].attributes.body
My name is Reina!

Nice to meet you✨

# data[].attributes.body
I'm Misaki at Reitaku university.

I wanna enjoy this project.

I like pears.🍐♡

# data[].attributes.body
Nice to me to you.

I’m Yuna.

I am IEC major.

I live in Chiba, Japan.

I like outdoor activities.

# data[].attributes.body
My name is Natsumi. Please call me Natsumi. I am third grade. I hope everyone will have a nice time mapping. Nice to meet you.

# data[].attributes.body
I'm third grade. Please call me Honoka!

Nice to meet you.

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Hi,everyone!!

My name is Ayumi. I'm from Reitaku university.

Nice to meet you!!

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Hello〜😉

My name is Nana Tamai.

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Nice to meet you, I'm a student at Reizawa University!

My favorite food is rice!

Thank you!

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Hello

I’m Haruna.

I am a student at Reitaku University. Faculty of International Studies.

My favorite food is watermelon. thank you.

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I'm Ayane from Reitaku University, Japan.

Nice to meet you.

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Hello, I’m Yuta Miura from Reitaku University.

Nice to meet you!

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Hello, nice to meet you all!

I’m Chinatsu at Reitaku university🇯🇵

I wanna enjoy this project.

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Hello, everyone!!

I'm Miku from AGU. I'm currentry studying in Canada as an exchange student.

So excited to be part of this event!!

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Hi, I'm Riko Sueki from Aoyama Gakuin University, Japan.

Nice to meet you!

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This map shows volcanic activities in heatmap through different countries and populations.

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The best way to make maps on the internet.

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My name is Sandy Kim and I am a PhD student in the Department of Urban and Regional Planning at the University of Hawaiʻi at Mānoa. Happy mapping!

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This map shows the location of different types of disasters and total fatality by year. The colors represent the types of disasters (earthquake, flood, storm, volcanic activity, and mass movement(wet)). The size of the circle represents the number people who died in the event: the bigger the circle the larger the number of death. Transparency shows the year: the darker it is the more recent it happened. Lastly, the red numbers show the total damage amount adjusted for inflation in US dollar. It appears that the most damaging disaster is earthquake, followed by flooding and storm. (You can click on chart on the left to see the graphs) Link to ArcGIS Online Map: https://uhm.maps.arcgis.com/apps/mapviewer/index.html?webmap=7d8d9f4cc3e24e7fba7677d6faad36c3

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Sandy Kim, University of Hawaiʻi at Mānoa

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Hi, everyone! I’m Kouna Fukuda from Aoyama Gakuin University, Japan. Nice to meet you☺️

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Density analysis of flood events at global scale

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Density analysis of flood events at global scale

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Aloha!

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Carter Beale, University of Hawai'i at Manoa

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Mapping Countries Vulnerable to Emergency Events

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Mapping Countries Vulnerable to Emergency Events

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Vulnerability to natural disasters is often exacerbated by factors such as poverty, inadequate infrastructure, and limited access to resources and emergency services. I was interested in exploring how the number of natural disasters in a country corresponds to the loss of human life that takes place. Addressing the root causes of vulnerability and enhancing disaster preparedness and resilience are crucial steps in reducing the toll of natural disasters on human lives in the most affected countries worldwide.

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Luke Shideler, University of Hawai'i at Manoa

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Mapping Countries Vulnerable to Emergency Events

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Understanding the impact of natural disasters

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Aloha all! Sending everyone some warmth and sunshine. My name is Pauline Yang, and I am a student currently taking classes with the University of Hawai'i at Manoa. This is my first time in a Mapathon so it's real exciting to see what it is about. :)

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People Affected by Drought-Induced Famine in Africa 

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People Affected by Drought-Induced Famine in Africa 

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I sorted the disaster data to only count famines that were caused by drought. Then showed the total number of people affected per country in Africa since 1900. Here is a link to the web map: https://uhm.maps.arcgis.com/apps/mapviewer/index.html?webmap=28176c54e4174102a68da16c3dcd731c

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I was interested to see how disasters impact homelessness. I used the disaster magnitude and homeless data to create this map. Link to map: Link to Map: https://uhm.maps.arcgis.com/apps/mapviewer/index.html?webmap=3ef2a27acae24ae2af46e9fb266d3ec8

The data of homeless people is based on the people requiring shelter based on number of homes destroyed x 5. As one example (visible in popup data in the map), the large earthquake (7.6) in Pakistan left an estimated 5,000,000 people in need of shelter and over 73,000 people dead. The largest magnitude of disasters were in Asia/South Asia.

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Jolie Wanger, University of Hawaii at Manoa