Statistically Speaking

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  • Statistically Speaking is the Office for National Statistics' podcast, offering in-depth interviews on the latest hot topics in the world of data, taking a peek behind the scenes of the UK’s largest independent producer of official statistics and exploring the stories behind the numbers.
    © 2024
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  • Green Data: Measuring the Environment

    Green Data: Measuring the Environment
    2024/08/02
    In this episode we explore how the ONS measures our natural environment and the green economy. Relevant datasets: ONS Environmental Accounts Transcript MILES FLETCHER Welcome again to Statistically Speaking, the official podcast of the UK’s Office for National Statistics. I'm Miles Fletcher and this time we're getting back to nature as we explore the work of the ONS in measuring the economic and social value of the natural environment. Is classical economic growth - as measured by gross domestic product or GDP - always achieved at the expense of the environment? What price can we put on the amenities our environment provides? What is the green economy and what are green jobs? And what are the key data to watch as policymakers strive for net zero carbon emissions, while also seeking to improve national prosperity? Our guides through the rich and perhaps under explored landscape of environmental data are ONS’s Deputy Director for Environmental Statistics Analysis, Ian Townsend; Head of Natural Capital Accounts, Gemma Thomas; and Sophie Barrand, Monetary Accounts lead in the Environmental Accounts team. Welcome to you all. Ian to come to you first. The ONS is mainly known for measuring the economy and the population of the UK. So, what exactly is its role when it comes to the environment? What are we seeking to achieve? What do we do? What do we publish? IAN TOWNSEND So the environment is quite a broad topic that links with a lot of other issues and a lot of different national and devolved government departments and other related bodies producing statistics on the environment. And with all that range of statistics, we tend to focus at the ONS on the intersections between our environment and both the economy and society. This includes measuring what we call the Low Carbon and renewable energy economy, how many green jobs there are, the greenhouse gas emissions produced by different economic sectors, and valuing the services that nature provides to us, as well as providing rapid insights into what people and business think about climate change in the environment and their actions or indeed otherwise. MF And what are the major publications that come out of the ONS that people ought to be looking at to get a sense of what we're saying about the environment and its value? IT So I mentioned a couple in the introduction there - things like low carbon and renewable energy economy, green jobs, etc... and our emissions figures. But perhaps one that is quite worth bringing to the fore is our natural capital accounts. So, it's something we've done for several years, which basically looks at the value that ecosystems provide to nature and ecosystems provide to us, and the services that provides. So, we bring this out as a report every year - have done so for several years - and that looks beyond the economy, beyond gross domestic product, to look at all those natural resources and we found that in 2021, the total value of all those natural assets was around one and a half trillion pounds. It’s such a big figure, I think it can be quite hard for people to grasp. But a useful comparison might be that it's not that far off the 1.7 trillion pounds that homes in the UK were valued at in 2021 as well. MF It's very difficult to arrive at a financial figure or value like that. Can you just give us a brief explanation of how it’s calculated? IT Sure. So, there are internationally agreed guidelines that we follow around how to measure or indeed account for the current value of what natural capital could provide for us and our current and future generations. And all that process, all those guidelines are aligned with how we measure the GDP in the economy. It's really quite a complex exercise and includes things like the value of trees, rivers, peatlands, and many other habitats and natural resources in them. We've been developing and improving these approaches for probably at least 10 years, and probably have some of the most developed accounts in this form globally. Our estimates have improved over the years. But there are some things that we don't cover. So, in a way, this is probably best seen as a kind of partial and minimum value, even though it's already very large. And it's also part of a wider mission that the ONS has to capture the value of what's called missing capital, things that we don't currently measure so well in gross domestic product. So that's including social capital as well as natural capital. So that's called ‘inclusive wealth’ and that's another publication the ONS produces that people might be interested to have a look at. MF And it's important, I guess, to have this economic value of the environment so that can be compared against the traditional measure of economic progress and prosperity, which of course is GDP. And it's sometimes – and we've heard this in other podcasts - because GDP is like...
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    35 分
  • AI: The Future of Data

    AI: The Future of Data
    2024/05/20
    With the public release of large language models like Chat GPT putting Artificial Intelligence (AI) firmly on our radar, this episode explores what benefits this technology might hold for statistics and analysis, as well as policymaking and public services. Joining host, Miles Fletcher, to discuss the groundbreaking work being done in this area by the Office for National Statistics (ONS) and across the wider UK Government scene are: Osama Rahman, Director of the ONS Data Science Campus; Richard Campbell, Head of Reproducible Data Science and Analysis; and Sam Rose, Deputy Director of Advanced Analytics and Head of Data Science and AI at the Department for Transport. Transcript MILES FLETCHER Welcome again to Statistically Speaking, the official podcast of the UK’s Office for National Statistics. I'm Miles Fletcher and, if you've been a regular listener to these podcasts, you'll have heard plenty of the natural intelligence displayed by my ONS colleagues. This time though, we're looking into the artificial stuff. We'll discuss the work being done by the ONS to take advantage of this great technological leap forward; what's going on with AI across the wider UK Government scene; and also talk about the importance of making sure every use of AI is carried out safely and responsibly. Guiding us through that are my ONS colleagues - with some of the most impressive job titles we've had to date - Osama Rahman is Director of the Data Science Campus. Richard Campbell is Head of Reproducible Data Science and Analysis. And completing our lineup, Sam Rose, Deputy Director of Advanced Analytics and head of data science and AI at the Department for Transport. Welcome to you all. Osama let's kick off then with some clarity on this AI thing. It's become the big phrase of our time now of course but when it comes to artificial intelligence and public data, what precisely are we talking about? OSAMA RAHMANSo artificial intelligence quite simply is the simulation of human intelligence processes by computing systems, and the simulation is the important bit, I think. Actually, people talk about data science, and they talk about machine learning - there's no clear-cut boundaries between these things, and there's a lot of overlap. So, you think about data science. It's the study of data to extract meaningful insights. It's multidisciplinary – maths, stats, computer programming, domain expertise, and you analyse large amounts of data to ask and answer questions. And then you think about machine learning. So that focuses on the development of computer algorithms that improve automatically through experience and by the use of data. So, in other words, machine learning enables computers to learn from data and make decisions or predictions without explicitly being programmed to do so. So, if you think about some of the stuff we do at the ONS, it's very important to be able to take a job and match it to an industrial classification - so that was a manually intensive process and now we use a lot of machine learning to guide that. So, machine learning is essentially a form of AI. MILES FLETCHERSo is it fair to say then that the reason, or one of the main reasons, people are talking so much about AI now is because of the public release of these large language models? The chat bots if you like, to simpletons like me, the ChatGPT’s and so forth. You know, they seem like glorified search engines or Oracles - you ask them a question and they tell you everything you need to know. OSAMA RAHMANSo that's a form of AI and the one everyone's interested in. But it's not the only form – like I said machine learning, some other applications in data science, where we try in government, you know, in trying to detect fraud and error. So, it's all interlinked. MILES FLETCHERWhen the ONS asked people recently for one of its own surveys, about how aware the public are about artificial intelligence, 42% of people said they used it in their home recently. What sort of things would people be using it for in the home? What are these everyday applications of AI and I mean, is this artificial intelligence strictly speaking? OSAMA RAHMANIf you use Spotify, or Amazon music or YouTube music, they get data on what music you listen to, and they match that with people who've been listening to similar music, and they make recommendations for you. And that's one of the ways people find out about new music or new movies if you use Netflix, so that's one pretty basic application, that I think a lot of people are using in the home. MILES FLETCHERAnd when asked about what areas of AI they'd like to know more about, more than four in 10 adults reported that they'd like to know better how to judge the accuracy of information. I guess this is where the ONS might come in. Rich then, if I could just ask you to explain what we've been up to, what the Data Science Campus has been up to, to actually bring the power of artificial intelligence to our ...
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    34 分
  • Communicating Uncertainty: How to better understand an estimate.

    Communicating Uncertainty: How to better understand an estimate.
    2024/03/25
    The ONS podcast returns, this time looking at the importance of communicating uncertainty in statistics. Joining host Miles Fletcher to discuss is Sir Robert Chote, Chair of the UKSA; Dr Craig McLaren, of the ONS; and Professor Mairi Spowage, director of the Fraser of Allander Institute. Transcript MILES FLETCHER Welcome back to Statistically Speaking, the official podcast of the UK’s Office for National Statistics. I'm Miles Fletcher and to kick off this brand new season we're going to venture boldly into the world of uncertainty. Now, it is of course the case that nearly all important statistics are in fact estimates. They may be based on huge datasets calculated with the most robust methodologies, but at the end of the day they are statistical judgments subject to some degree of uncertainty. So, how should statisticians best communicate that uncertainty while still maintaining trust in the statistics themselves? It's a hot topic right now and to help us understand it, we have another cast of key players. I'm joined by the chair of the UK Statistics Authority Sir Robert Chote, Dr. Craig McLaren, head of national accounts and GDP here at the ONS, and from Scotland by Professor Mairi Spowage, director of the renowned Fraser of Allander Institute at the University of Strathclyde. Welcome to you all. Well, Sir Robert, somebody once famously said that decimal points in GDP is an economist’s way of showing they've got a sense of humour. And well, that's quite amusing - particularly if you're not an economist - there's an important truth in there isn't there? When we say GDP has gone up by 0.6%. We really mean that's our best estimate. SIR ROBERT CHOTE It is. I mean, I've come at this having been a consumer of economic statistics for 30 years in different ways. I started out as a journalist on the Independent and the Financial Times writing about the new numbers as they were published each day, and then I had 10 years using them as an economic and fiscal forecaster. So I come at this very much from the spirit of a consumer and am now obviously delighted to be working with producers as well. And you're always I think, conscious in those roles of the uncertainty that lies around particular economic estimates. Now, there are some numbers that are published, they are published once, and you are conscious that that's the number that stays there. But there is uncertainty about how accurately that is reflecting the real world position and that's naturally the case. You then have the world of in particular, the national accounts, which are numbers, where you have initial estimates that the producer returns to and updates as the information sets that you have available to draw your conclusions develops over time. And it's very important to remember on the national accounts that that's not a bug, that's a feature of the system. And what you're trying to do is to measure a very complicated set of transactions you're trying to do in three ways, measuring what the economy produces, measuring incomes, measuring expenditure. You do that in different ways with information that flows in at different times. So it's a complex task and necessarily the picture evolves. So I think from the perspective of a user, it's important to be aware of the uncertainty and it's important when you're presenting and publishing statistics to help people engage with that, because if you are making decisions based on statistics, if you're simply trying to gain an understanding of what's going on in the economy or society, generally speaking you shouldn't be betting the farm on the assumption that any particular number is, as you say, going to be right to decimal places. And the more that producers can do to help people engage with that in an informed and intelligent way, and therefore mean that decisions that people take on the basis of this more informed the better. MF So it needs to be near enough to be reliable, but at the same time we need to know about the uncertainty. So how near is the system at the moment as far as these important indicators are concerned to getting that right? SRC Well, I think there's an awful lot of effort that goes into ensuring that you are presenting on the basis of the information set that you have the best available estimates that you can, and I think there's an awful lot of effort that goes into thinking about quality, that thinks about quality assurance when these are put together, that thinks about the communication how they mesh in with the rest of the, for example, the economic picture that you have, so you can reasonably assure yourself that you're providing people with the best possible estimate that you can at any given moment. But at the same time, you want to try to guide people by saying, well, this is an estimate, there's no guarantee that this is going to exactly reflect the real world, the more that you can do to put some sort of numerical...
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    33 分

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Statistically Speaking is the Office for National Statistics' podcast, offering in-depth interviews on the latest hot topics in the world of data, taking a peek behind the scenes of the UK’s largest independent producer of official statistics and exploring the stories behind the numbers.
© 2024
政治学 数学 科学
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