Awesome.  Maybe talk about fire, or how to shave a yak. Who here has heard of the term yak shaving? Yeah, that's most of you. Who here has done some yak shaving this week? Ah, that's a fair number of hands, not as many as I'd expected. For those who don't understand the term and are confused, yak shaving refers to the series of seemingly endless tasks that you have to do. For example, you have to do task A, but in order to first do task A, you have to do task B, but you realize there's a prerequisite for that and you have to do task C. And when I mentioned this term yak shaving to my girlfriend's knitting group, they were mortified. And they yelled at me, you don't shave yaks. And I was like, okay, this is just what we do when we're in the DevOps world. I'd like to introduce you to someone. This is Sophia Weiss. Sophia is an actual yak farmer. She has this wonderful farm down in Ashland, Oregon, which is on the California border. And she graciously shared some tips about raising yaks with me. I'd also like to introduce you to JF Carcada B136. Yeah, cute yak, amazing name, right? Just rolls right off the tongue there. So I asked Sophia about the names, right? Like that's a ridiculous name. And she explained that the first two letters are actually a farm code. It's where the yak was born. And then it's the yak's name. And then there's an ID number. And the ID number actually encodes the year that the yak was born. She also explained that yak farmers track the sire and the dam. That's the father and the mother of the yak. Because tracking the lineage of a yak explains why a yak is the way it is. Not just the physical appearance, like what the fur is like, but also how it behaves. So when you're actually going about your yak shaving, you should track your tasks, not just what it is, but where it came from. What is the higher level goal that this is supposed to unblock, right? When was this made? Who made it? Because without this information, you lose the context of why you're doing that task to begin with. How many times have you done that? You've started on yak shaving and you're doing a thing and why was I doing this? You never know when to stop going down that chain if you don't have the context. Now the group was right. The knitting group was right. You don't shave yaks because yaks grow this thing called down fiber. Down fiber is the super soft hair that's close to their bodies that keeps them warm for the winter. And they use a special tool called an undercoat rake, which is essentially a brush and that helps separate that fur out from the outer guard hair, which is the coarse hair on the outside of the yak. And so when you separate it out, it allows you to make the super soft yarn that knitters love. So you don't shave yaks because when you shave a yak, well then all of the hair comes off. You get that coarse outer hair mixed with the soft hair and you can no longer make that really nice yarn. It can be super enticing when you're working on tasks to build that perfect solution, architect that perfectly well-built application, but you don't need that to unblock your tasks. You need to learn to start to brush your yaks just like real yaks, only do enough to unblock what you were originally working on. Otherwise, you're just gonna end up creating more yaks as you try to build these perfect solutions. So I was talking with Sophia and she did mention that farms, just like technical organizations, there's always way too much to do. Way too much to do, not enough people to do it. They have incidents just like we do, unplanned incidents that cause extra work. And doing capacity planning, to have workers on hand really helps. Having the right infrastructure really helps. But even with all that, it all boiled down to she said that if you're dealing with fancy yaks, when you're dealing with complex systems, sometimes you just end up having to shave yaks. But the one thing that's notable about this is that shaving yaks does not mean what we think it means. It does not refer to the endless chain of tasks. It's this, it's flipping that table over, burning everything down and starting over. It's what I call extreme refactoring. Just do it again. Because you end up with naked yaks. Naked yaks have to grow hair. So as you start thinking about yak shaving, number one, track your yaks, track your tasks, understand when you should stop. Brush them, only do enough and then really avoid that shaving because shaving does not mean what we think it means. I'm Jason, I work at Datadog. We build observability tools. We have a ton of yaks that need some brushing. So if you're looking for new opportunities, if you like yaks, come talk to me later. Thanks.


Attention East Colfax residents. This is a community notification of a new sexually violent predator in your area. Please stay tuned to the following short program for more information. Hi I'm technician Ron Hackett with the Denver Police Department. The sex offender registration and compliance unit at DPD is responsible for the registration monitoring and tracking of all registered sex offenders and sexually violent predators who live in the city and county of Denver. Here's a brief history of sex offender legislation in Colorado. Our state laws trickle down from federal government mandates. In 1994 we had the Jacob Wetterling Act which mandated that states would establish a sex offender list. Magans law of 1996 said that the public would have access to those sex offender registration lists. The Pam Litchner Act of 1996 stated that some offenders might have to have lifetime registrations and the Adam Walsh Act of 2006 unified sex offender laws across the country. In Colorado the legislature has determined that sexually violent predators by definition pose a higher risk to the community at large and therefore mandated that upon their release from the Department of Corrections the community must be notified. Sex offender notification in Colorado happens in two ways. The first is passive and ongoing through the sex offender registration process which is a list maintained by local police departments. The second way is active via community notification for those offenders determined to be sexually violent predators by the courts or by the parole board. Most sex offenders in Colorado are supervised by the criminal justice system in our communities. Most sex offenders engage in crossover behavior which means they may have been arrested for one type of crime but be apt to commit another.  There is no typical sex offender but all tend to be deceptive manipulative and secretive. 80 to 93 percent of sex offenses are committed by someone known to the victim. Sexual deviancy begins in mid to late adolescence.  The community has a vested interest in helping offenders to be successfully managed in the community. Sex offenders have the same need for housing and employment as any other citizen.  There are approximately 16,500 registered sex offenders in the state of Colorado. About 65 percent of convicted sex offenders are placed in the community on probation. The remainder are in the Department of Corrections or Community Corrections. Offenders may be caught for one type of offense but be at a high risk to commit another type. Crime of conviction is only one indicator of risk.  The offender's residents must be verified quarterly by the Denver Police Department. The full sex offender registration list is available by calling the Denver Police Department Identification Bureau at<PHONE_NUMBER>. Convicted felons and those with multiple convictions are posted online at denvergov.org-police.   Now, here's a look at the newest sexually violent predator living in Denver. Here's an overview of the registered sex offenders currently living in Denver. There are 2,517 total living within city limits. In Denver Police District 2, there are 384. Precinct 223 has 95 registered sex offenders. Of those, 72 have felony convictions and 23 have misdemeanor convictions. Joseph Alonzo Parker is a sexually violent predator currently living in Denver. He's 42 years old and was born on June 24, 1979.  He's currently living at 8300 East Colfax Avenue, number 209 in the East Colfax neighborhood of Denver. Schools located within one mile of this offender include Denver School of the Arts High School at 7111 East Montview Boulevard, Odyssey School of Denver K-8 at 6550 East 21st Avenue, Montclair School of Academics and Enrichment at 1151 North Newport Street, Denver School of Science and Technology Middle and High School at 2000 North Valentia Street, Ashley Elementary at 1914 North Syracuse Street, William Roberts K-8 at 2100 Akron Way. Parks located within one mile of this offender include Crescent Park, McNichols Park, Denison Park, Thomas Park, Greenway Park, Montclair Park, Burbina Park, Great Lawn Park, New Freedom Park, Central Park. Recreation centers located within one mile of this offender include Montclair Rec Center at 729 Ulster Way. Bus routes located within one mile of this offender include routes 15 and 15 L East Colfax Avenue and Uinta Street.  He's currently on parole and registered with the Denver Police Department on April 7, 2022 to 8300 East Colfax Avenue, number 209 in the East Colfax neighborhood of Denver. Past charges against this offender include first degree sex assault, assault menacing knife, carrying a concealed weapon, robbery, second degree burglary, first degree trespass, theft, vehicle theft, traffic offense alluding, marijuana, contempt of court, possession display weapon, shoplift, property crimes, pawnbrokers act, receiving stolen property, making a false report, drug paraphernalia, smuggle contraband into a prison, failure to register as a sex offender, parole violation, violation of a restraining order, disorderly conduct, unreasonable noise, resist officer obstruct without violence, escape, attempt to escape, fraud, impersonation, grand larceny. Joseph Alonzo Parker has been determined as sexually violent predator after meeting the following criteria. Criteria one, age 18 or older on the date of the offense or under 18 and tried as an adult. The offender was age 21 at the time of the offense. Criteria two, the crime must have been committed honor after July 1, 1997 and convicted honor after July 1, 1999.  The offender was convicted on February 27, 2001 for sexual assault on a child. Criteria three, the relationship to the victim was one of the following. Victim was a stranger to the offender or the offender established or promoted a relationship with the victim primarily for the purpose of sexual victimization. The victim was a 13 year old female. The offender was known to the victim. Criteria four, positive results or the passing of a risk assessment instrument administered by probation parole staff and a qualified treatment evaluator. Positive results include an assessment for the presence of a mental abnormality, a level of denial regarding the offense, treatment appropriateness and motivation, presence of sexual deviant interests. The offender was evaluated by the Colorado State Board of Parole with positive results, determining him to be a sexually violent predator.


Hi, I'm Zor. Welcome to Unisor Education. Today we will talk about statistical distribution. This is part of the Advanced Mathematics course for teenagers and high school students. It's presented on Unisor.com and that's where I suggest you to watch this lecture from because the site has, for every lecture, it has detailed notes, which can be used just as a textbook, plus the site has functionality like enrolling, for instance, or taking exam, etc. So I do recommend you to go to unisor.com to watch this lecture. Now, statistical distribution. I have spent some time in previous lecture explaining what actually statistical distribution is. Well, basically in a couple of words is you have some random variable and you don't really know any distribution of probabilities of this random variable. Sometimes you know the values, but you don't know the probability. Sometimes you don't know even the values which this variable can theoretically take. So your task is, based on whatever the experiment results you have, whatever the empirical data you have at hands, evaluate the distribution of probabilities of this random variable and, well, using this gives you some picture about its future behavior, because that's what probabilities basically is about. So I have subdivided this relatively big task into smaller ones and today we will consider one particular task, which I call task A. The task when I have one particular random variable, which I would like to evaluate and I know in advance what kind of values it can theoretically take. For instance, if we are talking about rolling the die, we know that it's either one or two or three or four or five or six. That's the values which this random variable can take. So we know the random, the values of random variable, but we don't know the probabilities because maybe it's ideal die, in which case each probability is equal to one-sixth. Or maybe it's not exactly an ideal die and I would like to statistically prove or disprove that this is or is not ideal die. So that's an example. So, okay, so this is our problem. Evaluate the probabilities of known values which our random variable can take using the past experience. Now, what is the past experience? Well, very simply, we basically arrange the experiment with this particular random variable. Let's say we have arranged and different experiments. So we have n results. Now, we know that these are different values which it can take. So let's assume that certain number of times out of this n new one, the variable took value x1, new two times out of these n, random variable took x2, etc. And new k it took the value xk. But obviously that equality holds. So we have divided n results into k groups. These are results when the variable took x1, this is x2, and these are the number of results which it took, the very xk. Now, question is how can we use these numbers to evaluate the probabilities unknown to us, p1, p2, etc. pk. So these probabilities we do not know. Okay. Well, the first thing which comes to mind is let's use empirical frequency which is, for instance, x1 it took new one divided by n times, right? Well, this is empirical frequency of occurring value x1. And most likely that's the best evaluation of the probability p1, right? Because what is the probability? The probability is in at least the most commonly acceptable definition is some kind of a limit. This frequency is tending to as the number of experiments goes to infinity, right? So that's kind of a assumed understanding of what probability is. It's not really mathematical. It's kind of more logical, I would say definition of probability. So that seems to be like a good evaluation. So our task is, right now, number one to evaluate whether this is an unbiased evaluation, which means that considering the new one is actually a random variable, considering, you know, Xi's random variable, new one and new two, etc. They're all randoms, right? So considering this, question is whether the expectation of this is equal our p1 and it will be equal. So that's fine. Much more difficult problem is to evaluate the margin of error. Now, it's intuitively understandable that as n is increasing, the precision of this evaluation should also increase, which means it should be closer and closer to p1. Now, how close? That's what margin of error actually is talking about, right? So we will have to evaluate the margin of error. All right, so we can kind of methodize our problem. And I will be talking mostly about p1 because everything else is exactly the same, right? Okay. So what can we actually do about evaluation of this particular random variable? Well, here is what I suggest. Let's take another random variable beta, which is equal to 1 if Xi is equal to x1 and 0 if Xi is not equal to x1. So this is Bernoulli random variable and we have already spent some time analyzing the Bernoulli's random variable. And I do suggest you, if you don't remember this, go back to previous lectures. It's one of the previous lectures about Bernoulli distribution and statistical analysis of Bernoulli random variables. So I'm introducing this Bernoulli random variable. Now, what's the probability of? Now, the probability of beta equal to 1 is exactly the same as the probability of Xi equals to x1, which is p1, unknown to us, p1, right? Okay. Now, let's talk about this, our empirical frequency. Let's think about it. If we conduct our experiment n times, we have random variable Xi taking certain values. At the same time, random variable beta takes certain values. Which ones? ones or zeros? How many times in our n experiments beta took the value of 1? Well, that's the same number when Xi is equal to x1, which is nu1, right? Okay, so in our n experiments beta took nu1 times value of 1 and corresponding all other, which is capital N minus nu1 times, it took value of 0. Now, each experiment we can actually consider as a separate random variable which is independent of b and independent among themselves and identically distributed as beta. So I can have beta 1, beta 2, etc., beta n. That's how we usually model statistics, right? And experiments result in n different random variables, which are, they are independent and identically distributed exactly the same as beta. And then what I have, I have some number of them, exactly nu1 number, which happened to be 1. And the rest is 0. So their sum divided by n, which is their average value, is equal to nu1 divided by n. Which is exactly what we want to use as a variation of our probability P1. So right now, basically instead of talking about nu1 divided by n as a random variable, which is an approximation of constant P1, we can talk about this as approximation of P1. Now, this is much more familiar territory because this is something which we have already considered before in the lecture when I'm talking, I was talking about random variables with Bernoulli distribution, right? So we know a lot about this. So what do we know first? Let's just think about it. First of all, expectation of our empirical frequency, which is expectation of this, which is they're all identically distributed and they're all independent. So the expectation of their sum is sum of their expectations, which in turn is exactly which is an expectation of each one of them, which is expectation of B and B, I'm talking, sorry, beta, not B. And you remember, this is equal to 1 if c is, that's equal to 1 and 0 is if c is not equal to x1, sorry, x1, right? So this is P1 and this is obviously 1 minus P1. So the probability of beta, 1 or beta 2, etc., to be equal to 1 is exactly P1. So this thing is equal to P1, P1 times n divided by n. So what does it mean? It means that our variation is unbiased, which is very good. Now, another thing is what's the variance? Well, we know that variance of this is equal to 1 over n square times variance of beta, right? Because each one of them has exactly the same variance as beta. Now we know from the Bernoulli variables that this is equal to 1 over times n obviously, right? Because there are n of them. So the variance of beta is equal to P times 1 minus P, right? Well, actually P1. Now, right from here, we see that the precision is increasing as n goes to infinity, right? Because these are constants, they're unknown constants, but they're constants. And n is increasing, right? So in any case, our evaluation of P1 as new 1 divided by n, the empirical frequency of occurring value x1 is number 1, unbiased because of this. And number 2, precision is getting better and better as the number of experiments is increasing. Well, the only thing is we have to evaluate how good this particular precision is. We have to evaluate the margin of arrows, right? Now, the margin of arrow can be evaluated in a crude way.  So in case P is equal to 1 half, it reaches the maximum. It's a parabola, right? It's a parabola with horns down and in the middle it has the maximum value, right? And the maximum value would be when P is equal to 1 half, which is 1 quarter. So I can always say that this is less or equal to 1 over 4n, from which I can say that standard deviation of new 1 over n, which is square root of this, is less or equal to 2 square root of n. And from this, knowing the standard deviation, I can always devise the margin of arrow. Why? Well, because you see, this is an average of independent random variables, right? Which means we can use the central limit theorem and say that this is approximately normally distributed random variable. And for normal variables, we know that for instance with certain two level of 95%, we have margin of arrow not exceeding 2 sigma. So margin of arrow with 95% probability, or certainty rather, would be within 2 sigma, which is 1 over square root of n. Which is good. Well, let's just consider how good it is. Let's say you have a die, and you have, let's say, 100 rolls, right? You're rolling it 100 times. So n is equal to 100, which means that 1 over square root of n times 2, because it's 2 sigma, would be 1 tenths. So what you can say is that for instance, out of 100, you had 15 times number 1. So, which means that 1500 is empirical frequency of number 1 on the die. And your precision is one tenths. Well, is it good? One tenths, or actually it's 10 hundreds, right? Which means it's from 15 minus 10, which is 5 to 25 hundreds. So that's the probability. Is it a good evaluation? Well, absolutely not. I mean, if we are saying that the probability of one is from one, rolling the one is from 120 to one quarter, that's too wide an interval. We need one sixth, right? Because that's what ideal die mean. So what I mean is that this is, well, it's an estimate of the margin of error, but it's too crude. We would like to have a little bit better approximation. So what's the better approximation? Well, you remember we were talking about sample variance. Okay, so let's talk about sample variance, and maybe we will get a better variation. So let me try to go through the calculations of sample variation. By the way, all the calculations are in the notes for this lecture. So if I will make a mistake, I'm sorry, but in the notes it seems to be correct. So let's try. Now, you remember that if we have, let's go from generality. So you have, for instance, some random variable eta, right? So you have experiments. You have average value. Let's call it zeta-ramnets equal to, that's average, right? So the sample variance is you take this minus zeta-square plus the second minus zeta-square plus et cetera plus the last one. Deviation from the last one from empirical average. And you divide it by n minus 1. Remember this formula? So these are random variables. These are the average. So these are values, basically, which we have received from the experiments, from n experiments. And these are the evaluation of their expectation, actually, which means it's arithmetic average. So you have sum of squares divided by n minus 1 to make the evaluation unbiased. So that's what we're talking about, right? Okay, so let's talk about random variable beta, which we have. So what's the random variable beta? We have b1, et cetera, beta 1, beta n, results of experiments. Now, what is the average? Beta 1 plus et cetera plus beta n divided by n is this, right? These are 1 or 0, depending on whether our initial variable took the value of x1. So this is the number of times out of n, it took the value of x1, which is nu1 divided by n. And we know this, right? So what we have to do is we have to summarize beta j minus nu1 divided by n square, summarization for j from 1 to n, and divided by n minus 1, right? So that's our statistical evaluation of variance, sample variance, right? So what is it? Let's just calculate. So this is evaluation of beta. Okay, now we know that out of these n, beta took value 1 nu1 times, and value 0 n minus nu1 times, right? So I can say that this is equal to, we have nu1 times, it took value 1, and n minus nu1 times, it took value 0. So that's our sample variance of beta. Well, let's just open the parentheses. 1 over n minus 1 nu1 times 1 minus 2 nu1 square divided by n plus nu1 and nu1 square, so it's nu1 cube divided by n square plus, well, 0 minus, so that's nu1 square divided by n square, so it's nu1 square divided by n minus nu1 cube divided by n square equals. Now this is reduced with this, minus 2 and this would be 1 over n minus 1 nu1 minus nu1 square divided by n. Okay, fine. Let me write it a little bit more neatly equals 2. I would like to have the common denominator, so it would be n times n minus 1, and on the top it would be n times nu1 minus nu1 square, so it would be nu1 times n minus nu1. So that's the variance of beta, right? Now, okay, what we are interested is variance of this, which is an evaluation, an estimate of our probability p1, right? This is approximately p1. So what's the variance of this? Well, if we know the variance of beta, then the variance of sum is equal to sum of variances, right? n goes out with a square, so the variance of nu1 over n is equal to, this should be multiplied by n and divided by n square, so basically it would be this. And this is a variance of our evaluation. Now, from the variance we can always go to standard deviation, which is square root of this. Well, n can go out, square root of nu1 n minus nu1 divided by n minus 1. So that's my evaluation of sigma. And obviously again from rule of 2 sigma, we can say that 2 sigma would be a margin of error with 95% probability. Just out of curiosity, let's calculate what will be in this particular case of the die when I have 15 times occurring number 1. So I will have 1 over 100 square root of 15 times 85 divided by 99. So what is this? I didn't do the calculations, but approximately it's 4 times 936, so it's 6 divided by 10, so it's approximately 6 1000s. Okay, now you remember it used to be 110, our margin of error, right? Well actually I have to double it, it's 2 sigma, so it's 12 1000s. So it used to be one-tenths. Now it's 12 1000s, which is much better, obviously. Now with this evaluation, with this value of 2 sigma, I can say that it's 15 100, my probability, plus minus 12 1000s. Well that's better, which is 12 1000s, just a little bit bigger than 100, so it's approximately from 14 to 16 100. So that's my probability, which is much closer to 16. I mean obviously. So this precision is much better than the precision when I was using just that crude evaluation with 1 over 2 square root of them. Well basically that's it, and I do recommend obviously to go through a little bit more calculations to get a better evaluation of the margin of errors. But what actually have we accomplished is the following. Based on certain empirical information, the results of experiments with our random variable C, which can take one of these K different values. So based on the statistics which we accumulated, new 1, new 2, etc., we can evaluate the probability, which is obviously an empirical frequency, but not only we evaluate the probability, we also evaluate the margin of error using this sigma in 2 sigma sign. So using these numbers which we have, the experimental numbers, the total number of experiments, and the number of times it took the value x1, we evaluate the precision of new 1 divided by n as an estimate of the probability. Well obviously whatever I said about new 1 can be repeated for new K. So that's the methodology. You have to, number 1, accumulate statistics. Number 2, you have to evaluate empirical probabilities just basically dividing the number of times each value occurs to the total number of experiments. And then using formula like this, you evaluate the margin of error. What exactly the probability, what's the range of probabilities which you have evaluated using this methodology. Well that's it. Thank you very much. I do suggest you to go through the lecture again through the notes. It's very, very helpful. So other than that, good luck.


Good morning, ladies and gentlemen. Thank you, Ricky, for the introduction. There's a bit of overlapping between the Secretary for Development's presentation and mine, and I'll just try to flip through those that are overlapping with her. So, let's begin. This is the first time I participated in the Urban Age Conference. I really don't know what to expect of. So, I was given 10 minutes and 10 slides, something. I go beyond that in terms of slide, but I hope to keep to the 10 minutes. Now, I don't need to go through the basic statistics of Hong Kong because I'm sure that you've been bombarded with them over the past two days. I would like, although to point out this map below, this is a map produced by my department using remote sensing and GIS for the urban area. And I just want to highlight that all these are mountains in Hong Kong. Most of these development, urban development, are basically reclaimed land. Over the year, we have reclaimed almost 90% of Hong Kong Island and for various uses like the port in Hong Kong, the airport, and urban development and commercial development along the northern coast of Hong Kong Island. So, it is as much by design as it is by default that we have vertical development, high density and very compact urban form. I don't need to go through this. Now, we have a lot of problems and again, that's been highlighted in the morning so I don't really want to go into them, but just fit through it and we'll come back to it if you have any questions. Now, back in 2007, we have completed a strategic study for Hong Kong called the Hong Kong 2030 study. We roughly do a strategic study once every 10 years. We have adopted the so-called the integrated land use transport and environment framework to guide developments in Hong Kong. What this means is that actually back in the 80s, we use this transport model, land use and transport model as a guide for development. In the 90s, we realized that that is not enough. We have to look at environment right at the very beginning so we adopted this framework. What this means is that we conducted studies for instance for fish points. We have wetlands in Hong Kong which are frequented by migratory birds during the winter seasons. We've done study, we know the ecology of the wetland area, the fish points and then we draw up our plan to preserve the wetland and using the fish points as bubble zones and we also allow some development within the bubble zone. So these are, if you like, our ecological infrastructure of Hong Kong and we have also done landscape mapping for the whole of the territory. Just looking at the landscape value, the ecological value of the whole territory and then we draw up, in this study we draw up the so-called no-go area. These are the areas that we should not meddle with. Leave them as it is for research, for public enjoyment, for conservation to maintain biodiversity, so on and so forth and we have to focus on those developments, the land that are suitable for development. So we have a roadmap, sustainable development is our overarching goal for this strategic plan. I'll come to each of these topics later on. Right, spend a bit of time on this one, I think it's worth. This is the rail network of Hong Kong. Back in the 90s we have identified rail to make Hong Kong, to keep Hong Kong moving efficiently. We need to depend on public transport and of all public transport rail development is a backbone and we have this so-called transit oriented development on top of the rail station and this is what we have now and the green lines are in the pipeline. This will be completed, the extension will be 2014. To the south the red line here will be 2015. Also the shot into central link 2018 and 20, maybe 20, 20 across the harbor. So with all this new network I'm sure that this is what we have currently. This is what I did in the paper back in 2009 actually. The figure may have gone up a bit. Within 500 meters of our station we have 75% of our commercial and office space. Meaning jobs are concentrated there. Also if you look at other living quarters some 42% of housing units are within 500 meters of a rail station. Some of the housing development areas not really in the CBD core, the core CBD. They are on the outline area like Newtowns. But every day this will move people from going to work and back home. And I would like to point out one very important point. To make this successful you have to have very good design for the station and the connection. I'll come to that about our fridge network so that people can do multi-purpose trips. Like going to work, having the breakfast on the way, come back and do shopping before they go home. Either by on foot or by feeder surface we call them mini buses. Now with this integrated use and transport planning we have a very compact urban form. We have a high reliance on public transport and the secretary mentioned this morning about 89% of all trips, daily trips about 12 million, more than 12 million trips, 90%, 89% are using public transport. With high density and mixed use we have a very vibrant and diversified urban form. And it makes it easy for people to move around. Now this is the central map and we have a number of stations there. We have all this fridge linkage and subways and of course this mid-level escalator. For those who are familiar with Hong Kong view we realize that this now carry about more than 80,000 people a day going to work from the mid-level which is a residential area going down to central business district. In addition there are all other bridges. Some of these bridges are built by government, others are built by the private sector. We use incentive to provide them with extra plot ratio so that they can build the fridge, maintain it and operate it 24 hours a day for public use. One of our important strategy is the wise use of land resources in Hong Kong. We have very limited land resources. That's why we have to recycle the use of land like this Taikuxing residential area and also other areas. Previously shipyards or industrial areas and after the contamination of land we can use it for development. It's not avoidable to encroach on the Greenfield site sometimes but we have to be very, very prudent. So these are the new development areas in the new territories. Some of them are being used for open storage at the moment. Others are Greenfield sites but for those areas with woodland or mature trees we will preserve them as open space for the new development areas. These are not new towns. They are much smaller than sites. Our usual new town is more than well over half a million people. We are talking about 100,000 to 200,000 people for these so-called NDAs. Now I can skip through this. I mentioned earlier about the no-go areas. You'll be amazed with your in Hong Kong in the urban area. There's hustles and bustles, streets and all activities but about half an hour or 15 minutes drive you are in the country part. Beautiful landscape. And this is something the local community really treasure and we have to maintain them. So about 90% are living within three kilometers of the country part. Very quickly on the open space, we have the country part and other open spaces. And 80% of the population are living within 400 meters of the district park or open space. Actually for comprehensive residential developments they are self-contained in terms of open space and some communities facilities like primary schools and so on. It's not just the number matter, the quality of open space is important. What we're trying to do is to link up most of this open space into a network of open space. At the moment a lot of work is being done on both sides of the Victoria Harbour thanks to Christine and the others. Advocating the preserving of the harbour and now we're trying to build waterfront promenade around the harbour on both sides of the harbour. Now we can improve the built environment by doing better urban design. We have adopted air ventilation assessment at the plan making stage and for large projects we use AVA as well and I don't need to go through this. This is a summing up of what I just said, I'm running out of time but before I leave I highlight a critical issue as suggested by Ricky about a few days ago. What we are facing now, Hong Kong is a global city. We are the third largest financial centre after London and New York. Now according to the globalisation and world cities network, Hong Kong is also the third largest global city in terms of international connectivity with all these so-called advanced producer services. So we have a very strong demand for land for officers, that's why Carrie mentioned about the colonies our second CBD this morning. We have to reserve land for hospitals or data centre, they are all coming to Hong Kong. MNCs, I think according to the latest survey more than 6,000 of them in Hong Kong, either regional headquarters, regional office or local offices. So there is a huge demand for office space and other spaces but how do we create for them while maintaining or even enhancing our living quality, that is a challenge. And also very important I think is to ensure we continue to move people and goods in a very efficient manner. So this is we have to take into account. Last but not least, we have to cope with a very increasingly vocal community and diverse public views. The reason why I couldn't join the dinner last night was because we are in the hearing of a landfill site in Hong Kong and you can imagine we started meeting at 9 until 8 but there's still going to be two more meetings. So with that, these are not unique to Hong Kong but I'm sure we can pick them up in the discussion. Thank you very much. Can I just ask you two questions, thank you for that. But if you were to take Philip's presentation and obviously you haven't done the analysis, what is your sense, just sense about you have this extraordinary transport system anyway where roughly 90% use public transport. If you were to look at it in terms of education or income groups, do you think there's a considerable difference in terms of those 500 meter distances in terms of those access to public transport for different social groups? What's your sense? Well, I don't have, obviously I don't have the specific, but basically for the real station in the new territories, in the new town, they are basically public housing, so they're not well off, just ordinary people, they use public transport. But let me pick up that question raised by Professor Ho, he's left now, that Kerry is reluctant, was reluctant to answer. It's about this mobility thing, whether, you know, give them subsidies. This is exactly what the government is doing. For a very long time, all full-time students, primary and secondary students, have subsidies in public transport. And also for now we have a new scheme for people working full-time or part-time. They have 600 if they work full-time or 300 Hong Kong dollars for transport subsidies. Provided they are eligible in terms of certain income level and assets. And also for elderly people, 65 or above, and for those people with disability, they just paid two dollars, Hong Kong dollars, roughly what, 16 pens for one trip on the MTR, on the franchise buses, as well as on the ferries. So there's some subsidies to enable, if you like, from the social perspective that we have to ensure that people, less all, will still be very mobile within the system. And can I, again, follow-up question just to clarify to us. You showed the parks, and it's an extraordinary range of quality of parks, very, very close by to the dense structure of the city. And also increasingly number of, I guess, internalized public spaces in the city. Given the subject of this conference and given the notion that we're interested in health and well-being. Again, thinking of Christine's question yesterday to Dr. Chow and reversing it, given that you're in planning, I know you're not supposed to talk about other ministers or other departments' agendas, but I'm going to ask you to speculate anyway. What is the relationship in terms of people's health? Do you take that into account? Obviously it's good, but do you have any way of testing that, getting feedback in terms of that? We see it from, I think, outside as an extraordinary range of opportunities for people to just get out and let off steam effectively. But is this something which is part of your planning philosophy? This is what you said earlier on the local DNA. Hong Kong, no doubt, is a vertical development, and by the way, before I start explaining it, Hong Kong government operates as a team. I think Kerry is just joking about not meddling in other policy bureau. I, for one, have talked about these subsidies. It is not transport and housing bureau, it's the labor and welfare bureau. So we will basically government work as a team to tackle problems. But going back to your question, I think this local DNA, people no doubt the flat size of Hong Kong are pretty small by world standards. And people like to, you know, on Sundays, a lot of people do is to have tea with the extended family in a Chinese restaurant, Yamcha, and Chichat the whole morning. And also, it's very important that our country parks are so close by. Actually, a lot of people, just last Sunday, we have this charity walk in one of the country parks in Hong Kong. We have a few thousand people going there. So every year, there are more than 13 million people visiting our park. So it's as well utilized. This is a way to, if you like, compensate for the small size of the flat in Hong Kong. So, yeah. Thank you very much. I'm sure we'll come back to you. So if I could now ask Yitam Tiwari to come to the platform. Thank you.