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fcorowe.bsky.social
Prof. Human #Mobility & #Migration β€’ Geographic #DataScience β€’ #Spatial #Inequality πŸ™ŒπŸ½ Lead @geodatascience.bsky.social & @qmrg-rgs-ibg.bsky.social ✍️ Editor @region.bsky.social πŸ’» Project: https://de-bias.github.io/debias/t πŸ”— www.franciscorowe.com
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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. πŸ“ŠπŸš¦
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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? πŸ”Ž
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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. 🌍🚌
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It’s probably a generalisation but it’s often what I have experienced
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Similarly in academic it’s not that we want to put the big picture behind as a second priority but the methods and data often take priority
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This is not to say that policy makers are not interested in robust methods and data but the focus on the evidence puts methods and data on the background
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On the other hand, policy makers are much more focus on what all these sophisticated methods and data can tell us about the process they are interested.
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In academia we want to produce robust evidence so we spend quite a lot of time developing sophisticated methods and engineered data to achieve this
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Quick reflection it’s great to hear how your research is perceived from a very practical / policy oriented angle. It puts forward the tension and different focuses of academia and policy
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Looking forward to grabbing a copy!
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Chile 🀚🏽
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Interesting - haven’t heard about the conference before
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This is good to know. I now wonder in what context we care about zero flows.
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Exactly - they are total flows