fcorowe.bsky.social
Prof. Human #Mobility & #Migration • Geographic #DataScience • #Spatial #Inequality
🙌🏽 Lead @geodatascience.bsky.social & @qmrg-rgs-ibg.bsky.social
✍️ Editor @region.bsky.social
💻 Project: https://de-bias.github.io/debias/t
🔗 www.franciscorowe.com
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We have an amazing line-up of authors contributing to the new digital (computational) CATMOGs. First up is
Serge Rey: doi.org/10.18335/reg...
Coming soon are Levi's (@levijohnwolf.bsky.social), Carmen's (@carmen-cabrera.bsky.social) and more!
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CATMOG is a classic series of guides that introduce key concepts and techniques in modern quantitative geography. Here is the original series which documents methodological advances the mid-1970s upto the early 1990s qmrg.github.io/CATMOG/
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DEBIAS project 👉🏼 de-bias.github.io/debias/
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Also we should consider that the number of unregistered births has increased along with various streams of immigration, particularly the undocumented steams
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Having the 2024 census data will provide a better picture. We just need to wait a few more months
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This is the same source reported by MoreBirths. I am unsure how MoreBirths arrived to that .88 figure
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Latest report www.ine.gob.cl/docs/default...
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That does not seem right. The latest offical figure I could find indicates a TFR of 1.17 in 2021 and I compute the TRF for 2024 using provisional pop estimates from INE: 1.14. This is assuming pop numbers from 2021 adjusted by survival rates.
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More about IMAGO here: www.ukri.org/news/22-mill...
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This is great. I have been waiting for years for this work to be realised! Many thanks Josh. This will be a very valuable resource in so many areas!
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7/ Our analysis also highlights the need for responsive transit systems that adapt to behavioral patterns. From peak-hour commutes to suburban connectivity, these insights are crucial for sustainable transport. 🌱 #ClimateAction
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6/ Policy takeaways:
Prioritise areas >10 km from city centers with better bus routes.
Densify road networks where sparse, but avoid oversaturation.
Tailored solutions for varied rider needs improve service resilience. #SmartCities
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5/ Machine learning revealed nonlinearities:
Beyond ~10 routes, adding bus routes has diminishing returns.
Long trips (>0.5 hrs) bring more temporal unpredictability.
Understanding these thresholds can inform smarter transit planning. 📊🚦
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4/ Key findings:
Trips farther from urban centers (>10 km) show greater spatial variability.
Dense areas & bus route availability reduce unpredictability.
Both too few and too many roads can increase variability. #PublicTransport
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3/ Using 20M trips from 1.9M passengers, we examined peak-hour bus rides in Beijing. Two key questions were addressed:
1️⃣ What drives spatial & temporal variability?
2️⃣ Are there tipping points where behavior shifts? 🔎
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2/ Why it matters: To make public transport more attractive than cars, we need to understand:
How predictable are transit trips?
What factors influence this predictability?
This study reveals how built environments & travel habits shape bus use patterns. 🌍🚌