Joseph Ricafort

#CebuEarthquake #Aftershocks #PHIVOLCS #DataViz #GIS #DisasterPreparedness

Tools: Visualization created in QGIS, annotations in After Effects, Python scripting assisted by ChatGPT

We see where the aftershocks were happening, how strong they were, and how the sequence evolved.

Data source: PHIVOLCS Earthquake Information (Sept 30–Oct 2)
Note: This is an explanatory visualization, not a real-time alert. For safety guidance, always follow PHIVOLCS and your local authorities.

The first frames highlight the main shock, followed by notable M4.8 and M4.5 aftershocks clustered offshore. Over time, the animation shows how activity concentrated along the same fault.

Why visualize this? Earthquakes can feel chaotic in the moment. By mapping the data, patterns become clearer.

This animated bubble map shows the aftershock sequence from September 30 to October 2. Each circle represents one quake, with its size scaled to the magnitude. Each frame of the video corresponds to about 30 to 40 minutes, allowing us to see how seismic activity unfolded.

Cebu Aftershocks, visualized (Sept 30–Oct 2)

On the evening of September 30 at 9:59 PM, a magnitude 6.9 earthquake struck offshore northeast of Bogo City, Cebu. In the days that followed, hundreds of aftershocks rippled across the area, with consecutive ones recorded up to magnitude 4.8 and 4.5.

Flood risks are not just numbers—they’re lived realities. Data can help us see what’s at stake, and where accountability matters most.

#Philippines #FloodControl #DataViz #GIS #Corruption

🛠️ Behind the build:
Data: Global Flood Database + PSGC (Philippine boundaries)
Extraction: Google Earth Engine + AI-assisted scripting
Processing: QGIS + grid redistribution for population stats
Visualization: Observable Framework + DeckGL HexagonLayer

- In Mindanao, recurrent floods in Zamboanga del Sur, Davao del Sur, and Misamis Oriental highlight growing risks outside Luzon.

📊 Key Insights:
- Bulacan and Pampanga were among the most flood-prone provinces with around 6.5M affected in affected areas. Interestingly, Bulacan also had the highest number and cost of flood control projects—where many substandard or ghost projects were also later discovered.

🔍 How to read the visualization:
Color of the hexagons = frequency of flooding (the redder, the more frequent)
Height of the hexagons = number of people exposed within that grid
Hover to highlight provinces for details

Before corruption cases even surfaced a month ago, I built an exploratory visualization prototype to better understand flood risks across ASEAN. I mapped flood-prone areas and the number of affected people at a granular level.

Hello data explorers,

Flood control failures and corruption scandals take center stage in the Philippines. Budget for flood control projects have recently been reported down to zero according to Pres. Marcos for 2026.

Here's the catch, it is using an actual data which was taken from Yale University's Geographically based Economy Data: gecon.yale.edu

Would you want to have a postcard of it? Let me know if this is mini-project you are keen to explore further.

- The area of the hash patterns represent the actual cropland of the surrounding rural areas.
- The color theme shows whether you are situated within a tropical, subtropical and other vegetation types.

Here's my attempt to visualize someone else's city within a 1x1 degree of the world as a mini digital township.

Every visual element is encoded within 1x1 degree with:
- The size of your town or city represents by the actual population.

What if your town or city is visualized as a miniature isometric village? Can you imagine what your city in a postcard would look like?

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I was trying to organize my files and stumbled upon an archived project.

Like sculpting, you take raw materials, squish them together, trim the excesses and voila!

This is not how usual design and development process actually works, but for the sake of figuring out something new, let's be messy. XD

#deckgl #dataexploration #innovation

And the exploration alone is already a joyful process XD.

Speaking about mess, I basically dumped all potential data points I want to use for a potential 3d mapsploration, and potentially carve this to the point where it can be used for accessing key insights and make it even usable.

Mess, mess, mess...

I don't have a proper context to share yet and nothing fancy, but I'm playing around on Deck GL's capabilities to handle hundreds of thousands or even millions of data points. It's something that I've been longing to explore in a long time.

Let data guide your next trip (or analysis)

Explore the dashboard + map → traveltrendsph.vercel.app
#DataViz #PhilippineTourism #Observable #OpenData #GIS #TravelTrends #TourismInsights

📊 What I discovered
🇵🇭 95M recorded visits in 2019, 2021, and 2023
📉 Foreign tourism dropped 35% from 2019 to 2023
🏙️ NCR saw a surprising +240% surge in local tourism

👀 Many lesser-known places are gaining fast — are you watching them?

🛠️ How I made it
Cleaned shapefiles and tabular data from government PDF reports. Mapped and visualized using Observable Framework, Plot, QGIS, and Mapshaper

🌴 Find out which places Filipinos actually visit (with links to TripAdvisor Things to Do suggestions)
✈️ See which destinations foreign tourists love
📈 Track rising hotspots that could go viral next
🔗 Try it here: traveltrendsph.vercel.app

🚀 What I built
A visual and interactive dashboard of popular, trending, and underrated destinations across the Philippines, powered by real tourism data.

📉 That leaves a big gap between what’s known and what’s possible to explore with data.

So I asked: What if we made it easier to explore travel patterns across the Philippines using data?

🏖️ Where Do Travelers Go in the Philippines? 🇵🇭 | Philippine Tourism Data Explorer Travel Trends PH 🇵🇭

Why is so much valuable tourism data trapped inside PDFs?
📄 Government reports often bury crucial travel trends in hard-to-analyze tables.
👂 Meanwhile, travelers rely on word of mouth or Instagram to decide where to go.

An animated map of North America tracking wildfire smoke density, origins and displacement by wind. Published by the Financial Times.

A world map showing visa overstay rates for students and other exchange guests in the United States. Colored circles represent the percentages, revealing a trend of higher rates for many African and Asian countries. Published by Deena Zaidi.

A line chart comparing different projected scenarios for wind and solar energy capacities in the UK, showing how they are likely to exceed the current demand. Published by CarbonBrief.

A map of the world’s oceans with overlays of areas experiencing ocean surface heat waves in May 2025. Much of the map is affected, with a trend line showing peaks and a general rise in recent years. Published by The New York Times.

🔥 Our forests are burning, our oceans are dying, but at least solar energy is still on the rise! Dive into each topic and many more with the latest Data Vis Dispatch.

📊 www.datawrapper.de/blog/data-vi...

Done signing up!

If you're excited to explore your next Philippine destination using data, I’ll be sharing the dashboard page soon. Stay tuned!

#Philippines #travel #tourism #dot #Observable #dataviz #dashboard #exploration #lifestyle

It highlights both popular and trending destinations among locals and international visitors.

About the tool:
It's built using the Observable Framework, and setting it up was much quicker than I expected. The data cleaning took nearly a week (as expected!), but the Observable setup was fast.