Academic notes

Books read in 2024

Wed, 11 Dec 2024 00:00:00 +0000

It’s that time of the year.If you want to see what last time I did this looks like, head over here. Spotify just dropped Wrapped. The lists of the best everything in 2024 start rolling on. And I can’t help having a go at mine.You can see a slightly fancier version here. I’m reluctant to pay much attention to it because a) it counts my two dropped books as read and b) because… Goodreads. It’s 2024, your service is no better than in 2014. Most frustratingly, it could be sooo much better. Oh well, digression… One of the few practices I’ve kept over the years, even in the face of pretty substantial life style changes (e.g., baby) is reading. So, here’s my list of books from 2024. Let’s get all due caveats out of the way first of all. It’s technically not a “best of” but rather “all of”. I did not love all the books in there, but I did read them. Unlike many smart people profess, I have a really hard time giving up on a book I have started. This year though, I’m glad I stuck with all of them. Also, technically, I did not read all of them either. A good chunk of them I listened to while doing the dishes (more on this below). And, at the risk of stating the obvious, it’s December 11th., the year has not ended. I’m currently on three books (one, two, three) that I might or not finnish before the year is out. All are very good. Unless something radical changes, none would replace my picks this year. So, on we go.

If by best we mean a book that makes you stay up way too late reading, and you think about long after you’ve finished, the clear winner this year is The Deluge. It’s hard to state in words how much I liked what is a 900p. book mostly about climate change. Don’t be fooled though, this is not the type of sci/cli-fi where characters and story are simple vehicles for the Big Ideas. There are lots of those (Big Ideas), but there are also real characters. People you come to love, despise, and find uncomfortably close to you. Since I finished it, I’ve been looking for someone, anyone, who’s read it too and is up for a coffee, because there are so many bits I’d like to discuss with another human about this book. Unfortunately though, the search goes on…

The other book that surprised me in many and good ways was Doppelganger. I’m the proud owner of a 1,999 paper copy of NoLogo, which I devoured as a highschool student. After that, I figured there would be little in Naomi Klein’s writing I wouldn’t agree with or would change my views, so did not read more. How wrong I was. Admittedly, this is the most and least Klein book I can think of. It is something only her could have written and only now, after decades of craft. And is a really hard book to pull off. It’s about so many things (her confusion with Naomi Wolf, conspiracy theories, right-wing politics in the XXIst Century, doppelgangers in culture and art over history, Jewish identity…) that it is hard to classify or even summarise. But it’s well worth your time. Go read (listen to) it.

Honorary mentions. This has been a good year for reads. It’s also been a terrible year in many personal aspects of my life. In the worst of it in May, I picked up Where there’s a will, and I’m so glad I did. Chappell’s account of what it really means to win a race so brutal like the Transcontinental is everything I needed to read about human endurance, empathy, and humbleness. Impossible people was also a phenomenal (graphic) read. Wertz’s account of her own alcoholism is so raw, hopeful, and just pure funny. And last but not least, Devil in a blue dress. My friend Al turned me on to this, and I’m so glad he did. This is L.A. Confidential from the African-American perspective. It’s a great detective story, it’s so well written, and it’s an approach to race that, although written in the early 90s, reads just as fresh in 2024.

A couple of thoughts to close. First, almost all non-fiction I consumed this year was listened. I’d love to have the brightness and mental capacity to spend 40 minutes every night getting lost in thorough arguments. The truth is I don’t. Instead, I do have about that time every night when I wash the dishes and clean the kitchen. One day, in an ideal world, I’ll do all my reading, well, by reading. For what is worth, I do agree with the snob argument that reading is a better way to engage with writing than listening. It gives you more space to think and let the content change you. It’s just that such option is not in the cards right now. Second, I have tried to read as much from women as from men now for a few years, with varying degrees of success. This year I think I was fairly OK with it. This year, it’s almost been a perfect split between fiction (men) and non-fiction (women). I need more female fiction recommendations! And third, something I started this year is to systematically take notes and write down impressions as I was reading. I started towards the autumn, so I’ll reserve conclusions on that for the future (including whether the practice sticks at all!).

So, that’s it, one more year. If you know me a bit, you know I like talking books, and I’m always looking for more recommendations. So feel free to hit me up with books that have made your 2024 a better tour around the sun!

Books of 2024

Non-fiction

Where There’s a Will: Hope, Grief and Endurance in a Cycle Race Across a Continent, Emily Chappell.
Impossible People. Julia Wertz.
Doppelganger, Naomi Klein. [audio]
Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence, Kate Crawford. [audio]
When the heavens went on sale, Ashlee Vance. [audio]
The making of a manager, Julie Zhuo. [audio]
The bilingual brain, Albert Costa. [audio]
Hunt, Gather, Parent: What Ancient Cultures Can Teach Us About the Lost Art of Raising Happy, Helpful Little Humans, Michaeleen Doucleff. [audio]
The Book You Wish Your Parents Had Read (and Your Children Will Be Glad That You Did), Philippa Perry. [audio]

Fiction

Spook street (Slough House, Book 4), Mick Herron.
The tusks of extintion, Ray Nayler.
Devil in a Blue Dress, Walter Mosely.
Hum, Helen Phillips.
The Deluge, Steven Markley.
The Peripheral (Jackpot #1), William Gibson.
Time Shelter, Georgi Gospodinov.

Report on AI and Land Use

Thu, 14 Nov 2024 00:00:00 +0000

Geospatial AI for Land Use, by The Alan Turing Institute. Independent Report from The Alan Turing Institute - URL

How geospatial AI can help inform our land use choices. Blog post from the Geospatial Commission on the report - URL

TODAY, the Geospatial Commission released the report weThe team on this project spanned folks at the Commission and Urban Analytics@Turing. At Turing, Barbara Metzler and Stuart Lynn did most of the heavy lifting, and I was the lucky one to “see early demos and present it around”. prepared for a project we have been working over the last year in collaboration with them. The work builds on our earlier contribution to the National Land Data ProgrammeGeospatial Commission (2023). Finding common ground: Integrating data, science and innovation for better use of land - URL last year, and the document puts in writing much of what we presented to the minister for AI and intellectual property last March. There is also an accompanying blog post Mehul and his team at the Commission put out.

Most of the report is a summary of what we learned in two specific exercises and a series of engagement events. I’ll give you the two-sentence version of each here but, if you are interested, you really should grab the report as it covers them much more comprehensively. In one, we took our initial DemoLand app, a tool that helps users explore different land use scenarios and their effect on areas such as air quality or house prices, and embedded a chat interface powered by a large language model. The original tool provided access to a lot of data and modelling that would be hard to access for non-technical audiences otherwise, but it still required the user to “know their way around”. With the new chat-based interface, exploring the results is a much more conversational experience that can reach larger even audiences. In the second exercise, we digged into the guts of the models that power land use applications such as those in DemoLand. Typically, many of these require data that is collected and released more slowly than ideally required for decision making (e.g., Census or building cadasters). We explored complementing, or even replacing, these by widely available satellite imagery. Satellite data is definitely not a new thing, we’ve had metal boxes orbitting the Earth since at least the 1950s, but there are a few recent things that make them more appealing. The revolution in computer vision (and what is now also termed AI) we have seen in the last 15 years has changed what we are able to do with imagery, even that of limited spatial resolution. In this exercise, we explore foundation models for vision (a bit like the GPT in ChatGPT but for satellite data) to see how far we could push them. The result is a series of what I consider extremely exciting results but others might see as largely dry performance scores tables. You can get a summary of those in the report. In addition to these two exercises, we also ran consultation events (at AI UK’24 and online) with experts to get a broader view on the potential, challenges and immediate opportunities of geospatial AI for land use. I could summarise those, but I won’t do a better job than the report, so head over to its “Section 3” for that.

What will probably (I hope!) catch most attention is the “Recommendations” section. Here is where we brought together everything we learnt in these exercises to propose concrete steps forward. In particular, we mention five:

1/ Identify additional areas of opportunity for satellite data to build the value case for geospatial AI.

2/ Develop a Geospatial AI Toolkit for LLMs.

3/ Expand the conversation on national foundation models to land use and geospatial.

4/ Improve access to key computational and data resources.

5/ Promote knowledge sharing and cross-discipline collaboratio.

Some are self-explanatory and I suspect few will disagree with them (who is against more knowledge sharing?). Others bring to the front discussions that we think deserve more attention than they’re currently receiving. LLMs, for example, are not very good at geography (there is a reason why the second L is not a G!). Before we jump in and take them off-the-self, we think there is work to do to develop the “Geography curriculum” we’d like these models to know when they help folks on spatial domains. And others seem more obvious than they actually are. Suggesting in 2024 that satellite data be used for land use change may cause unreparable eye-rolls among land use experts who’ve been doing this in an academic context for several decades. Yet there is still very little of it that has made it into “production” at scale, particularly in non environmental and physical domains such as cities and societyThis is one of the core ideas that made Imago a winning proposition, so watch this space. . So think twice before sending your eyes upwards.

Above all, and ironically given that it summarises past work, we hope this becomes a conversation starter. There is so much exciting stuff that is happening in the world of AI that could have tremendous implications for land use modelling and management. What exactly and, specially, how are debates we have just begun and are far from done. But we think they are worth having and actioning on. To work.

RGS Award

Tue, 07 May 2024 01:00:00 +0100

Today, the RGS announced that I received the Taylor & Francis Award for “excellence in the practice and promotion of teaching in higher education”. In their social media, the expanded a bit:

Daniel has made exceptional contributions to the teaching and practice of quantitative geography in higher education globally. He has led the way in integrating data science into geography, utilising emerging digital data sources to enhance our understanding of machine learning and artificial intelligence algorithms.

Let’s get the easy part out of the way: I’m over the moon to receive this prize. For someone who did not start his career in Geography to receive anything, let alone one of the annual awards, from the Royal Geographical Society is beyond humbling and honoring. I have always thought of Geography as a welcoming discipline, a place (pun intended!) where our differences enrich us, where we can pursue our true interests irrespective of field-specific quirks, tradition, or academic inertia. This prize is living testament of such view. It is also specially meaningful because the award comes from a nomination from peers at the QMRG, the group I had the pleasure of chairing from 2017 to 2022. Whatever they tell you, what academics really want in life is peer recognition. This feels like a good milestone in that regard ;-)

While everything in the previous paragraph is genuine, I also feel a bit awkward about receiving the award as an individual. Nothing in what I have done in the “practice and promotion of teaching in higher education” has been alone. This is of course true in the figurative sense (standing on the shoulder of giants and all that), but also in a much more literal way. An example. The two pieces of work I am most known for are probably my GDS Course and the GDS Book. There are more, but they all have similar stories. The GDS Course grew out of materials I developed to teach my class when I first arrived at Liverpool in 2015. A lot of the content took heavy inspiration from what I had been soaking up in my time at ASU while working with Serge Rey, Luc Anselin, and the rest of that dream team that was the Geoda Center in the early 2010s. It also took important expiration from the Tempe days: all the code relied heavily on PySAL and related libraries that were (and still are!) developed as open source projects by a growing community. In 2018, I “compared notes” with Serge and Levi, we decided to pool our respective materials, give them “a light touch to make them seem more coherent”The degree to which “light touch” became an understatement will continue to haunt me as testimony of my/our naive optimism. and, five years later, the GDS Book saw the light of day. It’s a long way of saying, very little of what has made me famous came really from me. If anything, I was the the lucky one at the right place and right time, absorbing (and enjoying!) everything, piecing it together, and putting it back into the public. More like a Dj than a composer.

It is also very hard to disentangle individual contributions from the institutional context where they happen. If Liverpool had not been open enough to allow a fresh lecturer, coming from a different discipline, to fully redesign a course that consistently attracted a lot of students and seemed to be just fine, probably nothing of this would have happened. Also, if I hadn’t felt supported enough to undertake such changes on the first day of employment, nothing would have happened. And that was the case only because of the Geographic Data Science Lab.

So, as I’ve said elsewhere, if I can ask of you one thing while you have me in the spotlight, make it this: see past my individual contributions, which are few and made possible by a non-trivial amount of privilege, and celebrate the magic of people, communities, and institutions coming together to build a better future together. That is what this award is about, and what is truly inspiring.

Duranton's notes on Urban Economics

Tue, 11 Jul 2023 01:00:00 +0100

⊕The original tweet that inspired this post is here but, in case the “bird” decides to change things up, the slide deck discussed is available on this World Bank URL directly.

My usual doom scroll on the bird this morning took me to this slide deck by Gilles Duranton, “New Frontiers in Urban Economics Research: A personal view”. I wish more big names did this more often. The academic system leaves little formal room for speculation, personal views, and synthesis of the kind that does not fully fit on a journal article (though, incidentally, I think this one would quite nicely!).

Anyhow, here’re my notes from the slides (I was not lucky to be in the room…), most of them repdouced here as the original text with additional comments.A couple of caveats, these are partial in the sense they are biased and in the sense they are incomplete. They contain more my own thoughts as contained in Duranton’s words, much more eloquent I could possibly do anyway.

The talk is structured around two themes: recent improvements in urban economics, and what I’d call “currently hot topics”. The former is then subdivided into: modeling, data, methods. I am these days less close to the applications of Urban Economics, and my focus is more on “infrastructure” that supports the field (as well as a wide range of other areas such as government policy, or other fields such as geography, planning, or architecture). For that reason, I’ll probably say very little about the half that focuses on hot topics, other than I really wish more people in more fields (e.g., quantitative geography) did this kind of exercise as a matter of relative routine).

Modeling

This is the bit I’m less close to. I never wrote a model in my PhD, and there’s a good chance sadly (or not) I never will at this point. But I did find interesting Duranton’s point about a much more iterative approach to empirical research:

I’m pushing typical approaches to the extreme to show neither data nor model should dominate […] Ideally, use an initial model to guide the empirics and revise it as progress is made in the data exploration, etc

Data

This to me is the biggie in the slide deck (and in the last couple of decades of Social Science for that matter…). As Duranton puts it, “we keep winning the lottery data”. Some sources he flags as game-changer in the last few years:

-Remote-sensing
-GPS pings from cellphones
-(Scrapped) web data
-Firm/corporate/marketing (what some would call consumer) data
-Historical maps
-Administrative data becoming spatially referenced

Duranton ackowledges he may be forgetting some sources. To be honest, I spend most of my days thinking just about data and I can’t come up with much more, other that possibly synthetic data simulated or modelled from disparate observed sources…

The other point made in this section relates to the challenges these new sources bring. Not in those words, but his point is these data represent not only a lot more data, but also different kind of data that require different kinds of approaches. And we in the social sciences are being slow to wake up to this realisation (I think).

An important call to not fall prey of the lost keys joke:

Novel data is not a substitute for an important and well-posed research question, insightful modeling, and a good research design

Methods

In Duranton’s view, urban economic research is about:

i. Acquiring/obtaining data from a four-dimensional world (inc. time)
ii. Projecting/processing them onto a dataset with rows and columns of with numbers
iii. Estimating quantities of interest out of this table/dataset

Of the three, he notes ii. has received notably less attention. I agree with this. Much of the challenge of “democratising” new forms of (unstructured) data in the social sciences is really about finding ways to turn them intro (structured) tables they can then work with. Until then, we can hope folks will become ML wizards but my hunch is the majority won’t. Hence the need to work in teams (also noted in the slides), not only of urban economists.

The second strand of thought in this section is about methods that generate data, think predictions from ML models. His view on this:

Machine-learning approaches have made their splashy entrance into our toolkit both to create data (here) and extract patterns out of them. We need to learn to use appropriate approaches and use them appropriately

The bit that is not mentioned explicitly are LLMs (although hinted at through “textual analysis”), but much of that slide took me back to the “Future of Urban Analytics” panel we organised at the last AAG, where Seth Spielman rounded up his partitipation with the quote “the data is the model, the model is the data”.

Finally, a point about delineation:

Much extent research uses the spatial units it uses “just because”. This leads to a variety of problems (measurement error, MAUP, etc). The cost of delineating appropriate units for a given research project is no longer prohibitive and will increasingly become a requirement

It’s really refreshing to see this view through beyond methodological geography (where it’s been most of its raison d’etre for a long time) and taken so on board. It is one I obviously share, I think we’re at the dawn of a time where thinking about which spatial unit one should use will be a core step in every empirical project, and empirical (spatial) research will be notably better because of that.

Ps.

As I said at the beginning, above are the bits I find most interesting from the slides, but this does not mean they’re the more important ones, so do check out the slides yourself if you’re curious. They are chock full of recent references, which is another aspect I think we do not do enough of in talks (we do of course in papers, but there the focus is usually much more delimitted and less wide ranging than in this case).

My #SAD cocktail party spec

Sat, 25 Mar 2023 12:00:00 +0000

⊕This is my writeup companion to the first, inaugural episode of the GLaD Podcast. If you like it and want more like it, you should go listen to the episode (and tell a friend to tell a friend!)

You’re invited to a dinner party, a SAD dinner party. When you ask the hostess (Rachel) what you can bring, she suggests:

4 people. These are the only invitees at dinner, so prepare well.

This has been the hardest one to answer, so many people and so many reasons! In some ways it’s a good thing I have this problem of crowding (but it sure makes it tricky!). In the end, I’ve decided to have a real SAD dinner and hand-picked the four people I think have had the biggest impact on the SAD academic I am today. There were more, definitely other people I’d love to have dinner with, but I know dinner with these would be heartwarming as I’d be among people who appreciate me for who I am and have made me a better scholar:

David Cuartielles: this is my geek claim of fame, one of the co-founders of the Arduino project (look it up, it’s very cool!). I happened to run into David before fame as he was my Scouts leader when I was ten years old. The way he combined technology in creative ways, and his ever-travelling spirit (let alone his taste for long beards!) has stayed with me and influenced many big decisions I’ve made over the years.
Fernando Sanz Gracia: my PhD supervisor, someone who taught me how to let your students pursue their interests and, like good technology, “do your job and get out of the way”. I’ve always been good friends with him (still am!) but, as good wines, it’s been a relationship that’s got better and better over time because I’ve slowly caught up to realise how good a supervisor he was as I have become one myself.
Serge J. Rey: Serge is the personification of many of the things I like about the US academic culture. Mission driven, passionate, and placing the right amount of weight on having fun. He made my first visit to the US not be the last and was the start of a long-time friendship.
Luc Anselin: Luc’s ability to make the complex simple, and to see why space is relevant in so many contexts was eye opening for me, and it still informs many of the ways I approach research today. His ways of framing problems and motivating why space is relevant in today’s world have made me a better teacher, a better researcher, and a better communicator.

These are people I know I’d have a great dinner with. Some of them have never met each other but, like in a good wedding, you know they’d gel because they all have you in common. Hence my choice. There are honorable mentions that didn’t make it to the list because I don’t know them and, if it’s going to be only one dinner, I want to make sure. Yet, I’d like to give them a chance some time, if I have the option of a second dinner!

Hildegard von Bingen: apparently credited with being one of the first classical music composers in history, all the more remarkable given she was a female making music in the XIth Century. I’ve always wondered what personality would make that happen!

Malka Older: she was one of the first contemporary sci-fi writers I read and her ability to write fun stories full of big ideas has captivated me since then. Her profile as an academic writing sci-fi is also something that gives me hope maybe my career will take a turn one day that’ll make me more popular in cocktail parties!

Will Marshall: co-founder of Planet, the mini-satellites company that’s changing remote sensing. Every interview I’ve heard/read with him is full of insight and pretty empty of bullshit, a rare commodity in CEOs these days.

Matt Mullenweg: similar to Marshall, this is someone who’s main invention (Wordpress) runs the majority of the internet (!) and who’s building an empire on top of open source principles. That, coupled with his soft and sharp touch when interviewed, have made me really want to have dinner with him sometime!

1 book, in case conversation is boring and you need to entertain yourself

“Never lost again”, the story of Google Earth and Google Maps, was a really fun (pandemic) read I’d probably turn to if I was getting bored. Although thinking about it, this is a bit cheating because I never re-read books…

For the after-dinner drinks and discussion, you’re also asked to bring:

1 formative memory or experience

The first year of my PhD, I mostly just read papers and books. As part of the “training”, every week I’d meet my supervisor and discuss the most important bits I’d read. Pretty early on, Fernando started bringing me classical music: every meeting, I’d bring back the CD (note the time stamp on the statement!) he’d given me the week before, we’d discuss it for the first 10 minutes, and he’d give me a new one for the week ahead. I received his latest recommendation this week (this time in the form of a Spotify URL!) and we continue to exchange views. Sometimes, I even send him back some of my own obscure classical findings.

I picked this as a formative memory because, in addition to opening up the door of classical music, these exchanges taught me the importance of personal relations in Academia. Doing a PhD has an aspect that is not fully unlike a medieval apprenticeship: much is learned through “osmosis” as it were, by interacting with your peers and by absorbing from your mentors. Finding something that helps you bond over beyond the core topic of research is not only fun but also very important.

1 accident, fortuitous or otherwise

When I was a PhD student, I wanted to spend a year abroad visiting California (and perhaps picking up a bit of surfing). To dress it up as an academic endeavour, I found one Serge Rey at San Diego State University who was doing very interesting work at the intersection of open source and spatial analysis. It was a bullet proof plan. When my supervisor and I contacted him, he replied very positively and said he’d be happy to host me, but that he was moving to Tempe (AZ) to join the team of one Luc Anselin, and I’d be welcome there. It took me one query to the then new’ish Google Maps to realise my plan had just burst into pieces. No surf, but my career had been born with that email.

1 nugget of wisdom

There is a lot to learn from situations where you are outside of your comfort zone. As we progress in our academic (any?) careers, it is possible to narrow the scope of the area you work on, the people you interact with, and the situations you set yourself in. Staying a bit uncomfortable, trying out things we don’t master, or accepting challenges that (we think) go beyond our abilities is the best way I’ve found to stay young.

1 regret, professional or otherwise, for when the group has had a bit to drink and conversation turns inwards

This is one that took me a long time to find an answer for. In real or perceived ways, I think I’ve done fairly well “in the end”, so I find it hard to name something I really wish I had done differently. However, as I looked back, I realized there’d been several instances over the years where I did have regrets. For a long time, I wished I’d got my PhD abroad instead of at my home university. At times, I still wish I had taken more math and stats in college, which I didn’t on the belief that if I really needed them in the future (spoiler: you always do), there’d surely be time to pick them up along the way (second spoiler: the window for being able to find time and mindspace to absorb abstract ideas narrows faster than you think, particularly as you progress). But I’ve come around most of these by realising that, yes, I didn’t do those things, but I didn’t in part because I was doing other things that would help define me and shape the scholar I am today. I would have probably not met a supervisor with the dedication and time for me as Fernando, or I probably wouldn’t have had time to read as much urban literature as I did… There’s no free lunch kids.

In Desert Island Discs, the castaways are given the Bible and the complete works of Shakespeare as a given. I’d be curious what two books you think should be a given at any proper SAD dinner party.

I picked two that, ironically, are not very #SAD at first sight:

Gelman & Hill classic on multilevel models was the book that finally made so much of traditional statistics click in my mind and one I continue to recommend to all my students, even though it actually contains very little space/geography.
Chollet’s “Deep Learning with Python” is the best introduction I’ve read to get the intuition behind many of the methods that have radically changed “analytics and data” in the last ten years. Another one I try to get all my students to read. This one actually contains quite a bit of space, but in a covert way as most computer vision (e.g., convolutional neural networks) is actually all about space.
I’d also be curious what you’d want to eat for main course and dessert at this SAD dinner party.

Future generations will likely (and rightly!) loath my choice, but every once in a while (very rarely, I’m mostly vegetarian on regular meals) I enjoy steak tartare. I am also one of those people who start reading the menu from the end, pick a dessert, and work their way back to ensure whatever the rest is “allows” for the calory intake at the end. My favorite sweet is red velvet cake, whose original recipe includes beetroot. One of five a day.

Royal Society's Multifunctional landscapes

Thu, 09 Feb 2023 12:00:00 +0000

⊕These are notes from my reading of the executive summary of the Multifunctional landscapes report the Royal Society published earlier this week. The summary is really succinct and makes all the good points in five recommendations. These are some of my opinionated highlights.

Now is a critical time for land use policy globally (e.g., climate change, energy crisis), but particularly in the UK (e.g., Brexit). Science and innovation have several important roles in helping manage landscapes better (the report lists several of them in detail).

Recommendation 1 - embrace a multifunctional approach that considers multiple market and non-market land-based outputs.
Recommendation 2 - the critical role of research and innovation, which needs to be oriented to continue increasing the productivity and profitability of farming, but also expanded in three critical ways: take a multifunctional perspective (see R.1); pay much greater attention to sustainability; and carry out more research on landscape functions and how they interact with each other (trade-off’s and synergies).
Recommendation 3 - increase and adapt skills, training and advice for land managers
Recommendation 4 - “A novel data science-driven approach is needed to develop a high-quality common evidence base to underpin land use decisions”. This ons hits really close to home, so I’m just going to reproduce it here verbatim:

Decision-making in multifunctional landscapes will benefit from integration of information about all aspects of landscapes including farm and forestry outputs, biophysical data (such as topography, climate, soils and biodiversity) and ecosystem services, as well as socioeconomic data such as land values, land ownership and livelihoods dependent on land-based-activities. Some of this information is available but is of variable quality, relevance, timeliness and accessibility. A more strategic national approach to land-based information, with clear data standards and protocols and creation of a common evidence platform, would empower decision-makers with a consistent set of science-based inputs from which to negotiate land use decisions. It would also facilitate the incorporation of new data streams made available by technological advances. Consistent and scientifically robust baselines, metrics and systematic monitoring programmes are needed to better understand the state of landscapes and what they are being used for, and to track progress towards meeting policy objectives.

Recommendation 5 - We need spatially explicit national land use frameworks to judge the compatibility of different policy commitments. These need to be based on robust data and analytics, and be transparent.

The Royal Society (2023). Multifunctional landscapes: Informing a long-term vision for managing the UK’s land - Executive summary. ISBN: 978-1-78252-633-9. https://royalsociety.org/topics-policy/projects/living-landscapes/multifunctional-land-use/

Open by Default talk

Thu, 14 Jul 2022 01:00:00 +0100

⊕This text was originally posted as a Twitter thread

I’m super glad the lovely @RSSMerseyside/@hipyliv folks put together the “Using open data sources” event and made the recording available:

I contributed the talk “Open by default - Developing reproducible, computational research”. You can find more info about the talk at here. In this post, I’ll provide a bit of background.

⊕
Slide

For the last +2 years, Martin Fleischmann and I have been working on the Urban Grammar project, funded by ESRC and the Alan Turing Institute to develop new ways of understanding (urban) form and function through (satellite) data and machine learning (all project info at the project page). From Day 1, we had very clear we wanted to take an open approach to the project, and that this shouldn’t only apply to its outputs, but also to the process to get there. From that point onward, almost every decision we made in the project was guided by that principle. The talk covers in a (roughly) structured the areas we paid particular attention, namely aspects of process (a.k.a. the “kitchen”) and aspects of output (a.k.a. the “sausage”).

Our taken on “Open by default” (by no means invented by us!) is based on three key principles:

Slide

⊕The quote, like so much inspiration, has been blatantly stolen from a paper by Serge Rey (ironically, paywalled).

We’re not claiming this will work for every research project, but it has for a data/computation intensive one like ours. We have discovered many things we didn’t anticipate (mostly how useful it is even just for us to constantly work in the open), and this talk is an attempt to put a bit of structure into those thoughts. The key takeaways though are simple:

Slide

In academia, we talk a lot about outputs, but very little about process. How we do research and why different approaches yield different outcomes even if the results are seemingly the same matters. We should discuss it more, share best practices and learn from each other.

WUF 11

Thu, 30 Jun 2022 01:00:00 +0100

⊕
“Proof” I did go to WUF 11

This week I attended my first World Urban Forum (WUF), the UN’s biannual jamboree to celebrate cities, work done on cities, and work to do on cities. I confess that, for someone who’s be “doing cities” for the better part of 15 years, I’m a bit embarrassed it took me this long to show up to one of these. I’m glad I did. So, while it’s still hot in my memory and before I go back to the pile of email accummulated, I’ll try to commit to words a few of my impressions. Expect some ideas on cities, some on conferences in the new normal, and some on first-time conferences.

Start with things I’ve learned about cities. I leave Katowice with a copy of the 2022 World Cities Report, freshly released this week. More importantly, I leave with the determination to spend some quality time with the document, and giving some of the proposals in it the time they deserve. Some of the ideas I picked up from the press release are intriguing (e.g., “localising” SDGs), others exciting (e.g., a new social contract channeled through cities), and others I was more familiar with (e.g., 15-minute cities) but it’s fantastic to see they have found their place in a document of this caliber.

I focused most my time on sessions that touch directly my areas of expertise. Although not obvious at first sight, the WUF does bring together quite a few data nerds to discuss the potential and risks of weaving technology and data through the urban fabric. There were three highlights to me.

First, the session on the report on cities and AI.The report itself has not been published yet, but I look forward to reading it when it does come out It was great to see explicit brain power by a group of folks who collectively really understand both cities and AI. Unfortunately, the report itself has not been published yet, but I look forward to reading it when it does come out.

Second, the session on earth observation for SDGs, which showcased a bunch of really interesting projects in this space (pun intended!), including UN’s Earth Observation Toolkit, Prof. Yifang Ban’s Earth Enginge app for SDG 11.3.1, or the South African’s space agency efforts to build a “national GHSL”. My takeaway from this session however was brought by Dennis Mwaniki from UN Habitat who noted that there is a lot of work on SDG 11.3.1 (“ratio of land consumption rate to population growth rate”) and less in others (e.g., those on mobility and public transit), perhaps because it lends itself much more to be addressed with tools which have developed greatly over the last few years such as satellite technology. Food for thought.

The third highlight has to do with GHSL. The Global Human Settlement Layer is the term used to refer to the family of data products on human settlements on Earth that the European Commission (EC) and the Joint Research Centre (JRC) has been pushing on at least since 2016. At this WUF, JRC announced GHSL 2.0 which updates and widely expands the offering of datasets made available. There was special love for the Degree of Urbanisation product, which is now making its way into all sorts of policy arenas. To me however the most significant anouncement was the news that, from next year onwards, GHSL will transition from JRC into the Copernicus programme and will thus become part of the family of data that ESA and EC regularly take care of (think of it as a Sentinel mission for human settlements). As someone who’s been watching the new forms of data lanscape for a few years now and has seen many projects launch a flashy first release and then quickly wane, there is something very refreshing about a team making efforts to not only innovate on how we measure human activity but to ensure such advances have sustainability over time. Bravo!

Besides content, I learnt a lot about the “container”. WUF was my first big conference (+16k people) since COVID. It was live streamed for free for anyone to join remotely. In some ways, the space helped blend the hybrid experience: many sessions were on a (really) large hangar-like space that had been subdivided with very thin walls and no ceilings. Rather than piping sound into speakers for every room (which would make the whole thing quite the cacophony), sound was shared directly on the web stream and on headphones that everyone picked up at the begining of the session and left at the end for charging. Maybe this is standard in UN-land, but it was new to me, and I really liked it. It made the streaming, and live translation when available (main sessions) much more a first-class citizen experience because everyone was essentially augmenting their experience with headphones. It made it possible to have a lot of parallel sessions in essentially the same big space. And it also made bringing remote speakers (e.g., many folks saw their flights cancelled last minute) to a panel or a talk much more natural for similar reasons as above.

And a few thoughts on my own experience as a WUF newbie. The first is that the “UN vibes” that emmanate from all over the conference are just very cool. As an academic, I’m used to one very particular type of conference, and experiencing what a truly global community of policy makers, practitioners, and other academics excited about cities feels like was super nice. At times, I felt like I was in the middle of a Malka Older novel or in one of The Expanse episodes around the futuristic UN.

At the same time, this was a conference a bit out of my comfort zone and definitely beyond my usual networks. As a full professor who’s been regularly going to conferences for over a decade, it was at once refreshing, overwhelming, and humbling to be in one where you almost don’t know anyone. In some ways, coming to WUF made me (more) aware of the challenges for early career folk who attend their first conferences, and I hope I can take some of this feeling into action in events I’m more involved. Finally, it was also a good reminder that the temptation is big to think that, as we become more senior, we enjoy conferences more because we learn to “do conferences” better; but in fact, it is more likely that we find “our crowd” and limit ourselves to it for the most part. Stepping out of this pattern is challenging but important.

In summary, WUF 11 has been been a blast. I’ve learned a lot, met some very cool people, and left with a renewed sense of awe and urgency about working on cities. I’m glad I came and I hope it is not the last time.

Professor

Tue, 15 Mar 2022 00:00:00 +0000

⊕This text was originally posted as a Twitter thread

Last month I was promoted to Professor at Liverpool. To say I’m humbled is a bit of an understatement… It’s still a blur and suspect it’ll take a while to sink in. So, while that percolates, here’s a thought crystal clear in my mind: they call it “chair”, but professor really should be “bench”. If the “chair” is partly reward for the achievements of a career, it really would be best represented as a bench that can accommodate all the folks who’ve make it possible. And there’re always many of them! Part of it is the proverbial “giant shoulder-standing”, but there is also the more down-to-earth mentorship, collaboration, peer-support, and general “ass saving” academic networks provide when everything else falls.

In my case, I cannot think of a single achievement I’m truly proud of for which I’m solely responsible. It is impossible to understand my career without the context in which it’s happened, and that has always been one or another community or colective. So, while I truly am chuffed about the sound of “prof Dani”, let’s not forget how these successes are really made and the mechanisms and social infrastructure that makes them possible. And let’s nurture them accordingly.

Levelling Up & Moran's I

Thu, 03 Feb 2022 12:00:00 +0000

⊕This text was originally posted as a Twitter thread

Plenty of folks better placed to deliver hot takes so I’ll spare you mine… But, by my own account, the most statistically sophisticated fig in the full #levellingupwhitepaper is one on spatial stats(!). Gist: not wrong, not right either, but definitely cool!

The Figure…

The plot shows Moran’s I vs deprivation prevalence in Local Authorities. What’s Moran’s I? I’m glad you asked, this is a staple of Geographic #DataScience and spatial analysis courses alike (like mine! https://darribas.org/gds_course/. Moran’s I measures global spatial autocorrelation, capturing the degree to which values concentrate spatially. For a bit more on spatial autocorrelation, here’s me in my living room last year explaining pandemic-distanced students what it is in <10min:

Me, my living room, and spatial autocorrelation (check out the video here)

Moran’s I is also a way to “summarise a map” in that it assigns a value to a pattern of spatial concentration. Your map looks like this? Moran’s I is high and approaching 1…

High Moran’s I

Your map looks like this? Moran’s I will be low and approaching -1…

Low Moran’s I

By the way, these figures are created using a small simulation toolLaunch it on the cloud from here. Thanks to the voila team & @mybinderteam for the awesome infrastructure powering this! I wrote to help students grasp the intuition behind the statistic, and you too can play with it! For a 14 minute version of what is Moran’s I and why you should love it, welcome back to my living room!

Me, my living room, and Moran’sI (check out the video here)

This brings me back to Figure 1.23 in yesterday’s white paper, which took me a bit to decipher because it uses Moran’s I in a non-standard way. The point it’s trying to make is that cities w/ more income deprived population tend to display deprivation more spatially concentrated. This is an interesting pattern because it is saying that cities with more deprivation are also different in the way the deprivation is structured spatially (ie. more deprivation is related to more fundamental changes in how this is spatially configured).

But while the graph is not wrong… it is also not entirely correct. It is well understood that Moran’s I scores (the statistic itself, which is on the vertical axis of the figure) are not directly comparable across different geographies. The reason is a bit technical, but has to do with comparing different sample sizes and spatial configurations of values. A topic for another thread perhaps… More importantly, Moran’s I is not that useful as a deterministic statistic, as a score. A value of 0.3 can mean something very different in two different local authorities. This has to do again with the underlying assumptions of the statistic, but mostly because in some cases we will have enough evidence to say that value points to spatial concentration and, in others, it will be indistinguishable from a completely spatially random map. I’d have probably used any visual cue (eg. dimming non-significant values) to emphasize this aspect and bring more nuance to the figure.

Again, let me repeat the figure is not wrong. Using raw Moran’s I scores to gauge broad patterns is a sensible strategy to explore your data. Whether I’d choose to include the figure as it is on a final report is a different question…

So, to wrap up, although there are always ways to improve, I think it is awesome that policy makers are using these tools to inform their views on the world the decide on. This will be a very good example for prospective geography students on why they should take my numbers course even though what they really want is to “make a difference in politics”! Could it be improved the way these statistics are used in policy? Of course, but that is as much a job of policy makers as it is for us academics and data scientists “in the know”. The solution is not to remove the figure, but to have more of them, and to generate discussion around it so, over time, we all collectively get better at using data and stats for big policy “things”.

And that’s a wrap! If the nerdy bits of this post peeked your interest, consider spending a bit more time on the course websitehttps://darribas.org/gds_course or on the YouTube playlistYouTube Playlist ! See you on the next big policy paper that uses spatial stats!