#30DayMapChallenge Day 21: Conflict
At Kontur, we primarily analyze natural disasters, yet it’s clear that humanity often brings the greatest devastation upon itself. The worst destruction in war unfolds directly on the frontlines, where everything in its path is erased. Some regions, like the areas around Verdun, have yet to fully recover even after a century.
This map shows the cumulative months that each hexagon has been a frontline since 1900.
#30DayMapChallenge Day 14: A world map #Football (aka #soccer) reigns as the world’s most popular team sport, with massive budgets! 
️ This map showcases the estimated market value of national teams globally, based on the combined values of their players. 
#30DayMapChallenge2024
DATA
> Relative Wealth Index (https://data.humdata.org/dataset/relative-wealth-index)
PROCESS
1. Load the RWI point layer for the Philippines.
2. Style the layer as square markers—size: 2400 meters at scale; color: ramp of your choice.
30 DAY MAP CHALLENGE 2024 | DAY 8 - DATA:HDX
Relative Wealth Index
The Relative Wealth Index (RWI) from Meta are microestimates showing the relative wealth and poverty of different areas in a country.
IMPORTANT: As with any global-scale, machine-learning product, you should first validate the usefulness and applicability to your local context.
30 DAY MAP CHALLENGE 2024 | DAY 7 - VINTAGE
A (very maximalist) map of the University of the Philippines Diliman in vintage 16th/17th century European cartographic style (complete with some out-of-place sea monsters haha).
DATA
> UPD data
PROCESS
1. There are several ways to do this using QGIS, GIMP, or a combination of both.
2. Fantasy map brushes/pngs from @kmalexander (https://kmalexander.com/free-stuff/fantasy-map-brushes/)
30 DAY MAP CHALLENGE 2024 | DAY 6 - RASTER
Albay at different resolutions (100m, 500m, 1000m, 5000m)
DATA
> Any Digital Elevation Model (DEM)
PROCESS
1. Resample the DEM into 100, 500, 1000, 5000m meter pixel sizes (e.g. using Warp (Reproject) algorithm)
2. Create polygon boundaries for each resampled DEM using Polygonize (raster to vector) and Dissolve.
3. Style #2 accordingly
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DATA
> TomTom 2023 Traffic Index (https://www.tomtom.com/traffic-index/ranking/)
> DPWH RBI (for the roads) | You can also use OpenStreetMap
PROCESS
1. Style the main EDSA road differently from the other roads.
2. Add information in the Print Layout.
3. Utilize the Print Layout's ability to render text as HTML to style the texts.
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#30DayMapChallenge #30DayMapChallenge2024 #Day5 #Journey #MadeWithQGIS #QGIS #FOSS4G #TomTom #Traffic #Philippines #WorstTrafficInTheWorld #CommuterNaman
#KomyuterNaman
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But maybe we can start by shifting away from car-centric designs, investing in safe and efficient public transport, creating spaces for walking and cycling, and putting people at the heart of urban planning.
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#30DayMapChallenge #30DayMapChallenge2024 #Day5 #Journey #MadeWithQGIS #QGIS #FOSS4G #TomTom #Traffic #Philippines #WorstTrafficInTheWorld #CommuterNaman
#KomyuterNaman
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Every person on the road lost an average of 117 hours (~5 days) waiting in traffic [2] during rush hour over the course of the year [3].
Traffic is a wicked problem—so complex and intertwined that finding a single, definitive solution is virtually impossible.
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#30DayMapChallenge #30DayMapChallenge2024 #Day5 #Journey #MadeWithQGIS #QGIS #FOSS4G #TomTom #Traffic #Philippines #WorstTrafficInTheWorld #CommuterNaman
#KomyuterNaman
30 DAY MAP CHALLENGE 2024 | DAY 5 - JOURNEY
BYAHENG MAYNILA (loosely translated: Manila Trip/Journey)
In TomTom's 2023 Traffic Index, the Manila metro area [1] ranked the worst among 387 cities with an average travel time of 25 mins 30 secs per 10 kilometers.
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#30DayMapChallenge #30DayMapChallenge2024 #Day5 #Journey #MadeWithQGIS #QGIS #FOSS4G #TomTom #Traffic #Philippines #WorstTrafficInTheWorld #CommuterNaman
#KomyuterNaman
NOTE
1. The flood hazard data has invalid geometries/features. You can resolve this by fixing the geometries (takes a long time) or simply disabling the Invalid features filtering in QGIS processing settings.
2. Some areas have no flood hazard features. These are marked as NO DATA in the maps.
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3. Generate centroids from the output of #2 (either using the Centroids algorithm or Geometry generators).
4. To speed up and automate the process, I created a model that runs steps 1-3 above.
5. Style the output of 3 using: marker = hexagon, size = depends on population, color = depends on hazard level (Var). Utilize data-defined overrides/Assistant.
PROCESS
1. Use the "Sort" algorithm to create an ordered version of the flood hazard layer such that the features with high hazard level (3) will always be the first feature that will be matched in #2 below.
2. Run a "Join attributes by Location" between the population hex grid layer and the sorted/ordered flood hazard layer (output of #1).
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30 DAY MAP CHALLENGE 2024 | DAY 4 - HEXAGONS
Population ⬡ Flood Hazard
- larger hexagon = more people in the area
- redder color = higher hazard level
DATA
> Population density for 400m H3 Hexagons [Kontur] - https://data.humdata.org/dataset/kontur-population-philippines
> Flood hazard (100-year rain return) [UPRI/Project NOAH] - https://drive.google.com/drive/folders/10pCWTfU-gVuAbdx4gdUGaDcNrSzMz0Mm
30 DAY MAP CHALLENGE 2024 | DAY 3 - POLYGONS
Shapes of (K)yøu(si)
of triangles, circles, and ovals
DATA
> Digitized from OpenStreetMap (copyright OSM contributors)
PROCESS
> Duplicate the digitized layer.
> Apply a hand-drawn smudgy-pen outline style and a pencil fill with categorized symbology.
> Styles are from the hand-drawn styles by Andy Woodruff (https://www.facebook.com/bnhr.xyz/posts/pfbid02BKbYMnv5quZcDjNoYiGnZPzc2MqWwU8YbuypFJMdYA242WibaNnsZLsYErvWQEfdl)
30 DAY MAP CHALLENGE 2024 | DAY 2 - LINES
a thousand cuts.
Typhoon tracks from 1884-2024
DATA
> GADM country-level data (adm0)
> International Best Track Archive for Climate Stewardship (IBTrACS)
PROCESS
> Add spatial index to the 2 layers.
> Clip the IBTrACS layer with the GADM layer for the PHL.
> Style the two layers accordingly with
the PHL tracks slightly thicker.
30 DAY MAP CHALLENGE 2024 | DAY 1 - POINTS
The Philippine archipelago mapped using 10^n points with each point being 1000 x 2^(6-n) meters in diameter.
DATA
> GADM country-level data (adm0)
PROCESS
> Use the "Random points inside polygons" algorithm in QGIS to generate layers with 10^n points
> Style accordingly
> Add the 6 layers into a single Print Layout
