I'm going to talk about characterizing uncertainty in a kind of a broad sense, but really only broad within a destination of why you want to use Bayesian statistics. We have some classic assumptions whenever we're learning about statistics throughout much of our training and for many people still probably whenever you're doing a statistical inference. Homoscedasticity, so our variance is constant across time. The X variables and X being the predictor variables are in fact not variables, right? There's no error in them. All your errors in Y, and it's generally all crunched into the measurement error, right? So your residuals are things you just didn't count right. We generally assume that that's symmetric about the mean, it's normally distributed. Observations are independent and you don't have missing data. If you do that, that generally you lose a whole row, right? These are assumptions for many linear models and they have been associated with classic or frequent statistics that are really rigorous and if you can meet these assumptions. But we're generally designed for agricultural systems and well-controlled systems. I have a lot of colleagues that do things in jars. They put dirt in a jar and put it in the incubator and really tight control. But most of ecology really doesn't fit with those assumptions. So if you had this cloud, yeah, you can fit a linear model that goes through those data and you often would get something like an R-squared back out, right? So an R-squared might be of 0.6 and yet your slope is really strong, P less than 0.05 but you're still not explaining 40% of the variation in those data. If your goal is to get a statistically significant slope, that's great. If your goal is to make a prediction or forecast based on that slope, it's probably not great. And I'm going to, again, kind of ad nauseum go over some of the just terminology and the formatting to make sure we're kind of all on the same page, right? So this line here is a model. It's a linear model and in this case it's a linear model that has an intercept and a slope. Those are our beta terms and some error. We can think about this as both the data model where the y, our observations, are normally distributed. So that's the n with some mean mu and error and an error term, right? That's our observation model. And that's describing generally how big is that spread of our observations around that line. If we measured perfectly, our y's, they would fall on that line. If this model, our process model, was correct, right? So your process model is that linear model, okay? As you get more and more data, your beta and your beta not beta 1 parameter estimates get tighter, but not necessarily your observation error. Because we're working in the Bayesian framework, we also then have the parameter model. So our parameters in the data and process model, which are, we're going to be working with precision, so one over the variance. And the betas are themselves treated as random variables. That means that they are distributions that have their own parameters, which we will fix this one. For instance, we have a mean of zero in some center deviation is generally how we think of it, right? You've got your normal error, and then here we would have, again, a mean of the beta not or the beta 1 with some variance about that mean. So they each have their own variance. So in Bayesian framework, even our parameters are treated as random variables. And we're going to assume that they themselves have a distribution that we're going to describe. And we're usually trying to describe the variance on that distribution. And when I drew these lines, again, to emphasize that most of this spread is considered to be observation error. Given that model, the process model, if you could measure things or count them better, then they should fall on that process model, that line. All right, another way to write this is in graph notation. Again, we have our linear model up top. And what we're really saying is that we have a data model for how why we are assuming is sampled. We have a process model that has parameters, and then we have parameter models. Again, because we're working in a Bayesian framework, our parameters themselves have models or assumed to be from distributions. And we'll use this kind of terminology again and again. So if it doesn't look right, stop me. All right, and I kind of fell off the side a little bit. But again, this is reemphasizing that each of these lines has its own or series of equations that go with it. So beyond the classic assumptions, what are you actually going to do with real ecological data, which is why we're all here? First of all, I think it's useful for some people to back up again and think about what are we actually doing when we're thinking about a data model or a distribution at all? Where are we using a distribution? So we assume that the data, these bars, this histogram, are random samples from a true population or a distribution. And we describe that by probability distribution, right? So this one happened to be a normal distribution. And we describe it by a distribution because we can't sample all like so. So if we wanted to know the height of people in the room, we could measure that. But if we wanted to know the height of everybody who's interested in ecological forecasting, we would just use this group to estimate it, but we're not going to measure everybody. The distribution allows us to make some assumptions about how this group represents the entire population. So we all know that. Because there are some known, we generally pick distributions that have these known parameters, mu, standard deviation, things that describe the distribution that allow us more flexibility as we move further on to make comparisons. And it also helps us estimate what we didn't measure, right? So that kind of end down there. We didn't get any data for, but if we believe that the population is a normal distribution, then we can estimate what it should be, OK? So distributions, but not all data are normal. And so some distributions, again, even though they're not normal, I'm sure, you know, classic statistics has many very rigorous ways to deal with them. And I'm sure everyone in this room has used them. So we have a distribution here that's clearly not normal. It goes from zero upwards, right? So it's not continuous. Also, if you fit a normal distribution to these data, you know, if you do a statistical analysis as assuming that these data are drawn from a normally distributed population, you will get a mu and you'll get a sigma. But it's actually, right, so your mean here of three is not representing the data. It's not representing what was actually measured. I mean, it's wrong. So, but you don't necessarily know that, right, unless you plot them out. And so, you know, this is when you pick a different distribution that better describes that error or difference between the observations you collect and the assumptions about the true population. In this case, this would be a Poisson distribution, which is a general non-negative discrete data distribution. And instead of a mu, you describe a mean that now you can see is a little more representative of the data and make your assumptions about that. Incidentally, this is also a distribution that doesn't require homoscedacity, right? So the variance increases with the mean. So sometimes you can deal with that assumption just by choosing a better distribution. So we're going to kind of practice and talk about a lot about the distributions that we're working with for the different data sets as well as for the parameters. You know, are they realistic? Are you just choosing normal distributions? Because they multiply times each other really well, multiply by each other well.  So another common example of data that don't look normal, we've got binary data. And in this case, you can use a binomial or Bernoulli to look at the probability of a success, for instance, seed germination. So the probability in this case is treated with a logit, which is a transformation that puts the probability space, which is 0 to 1, into a normal distribution. And you can model that as a linear model, right? The important thing to kind of think about as we move forward is that is, you know, we've got our data model, and so we're assuming that, you know, that's not a normal distribution, that we are sampling out there from some true population of 0s and 1s, not a true population of 0.5s, right? So, but then we have this link that makes it linear and the process itself. And both of those together are part of your process model, right? So as you collect more data, you're getting a better and better estimate of that process model because we're Bayesian and we also have our parameter model. Another assumption that I touched on, homoscedacity, variance doesn't increase over time. One of these data sets, it gets a check, meets that assumption. One of them does not, right? So this data, I mean, we would test it to make sure, but I'm going to tell you it doesn't. These data look like the variance increases over time. That's a problem because it violates the assumption of a classic linear model. And there's a couple of different ways to deal with it. There's many different ways to deal with that. One would be to try to find a distribution that does not assume that the variance is constant over time, but another way would be to model it. You could model the variance. And so if you remember the last time I showed you a graphic notation, y is distributed normally with mean beta 1 plus beta 2x, right? So that's our linear model. And before we just had a variance term, right? And now you actually have a model for variance that's also based on x. So you're modeling the change in variance over that variable x. And the way it looks in the graphic notation is you just add in parameter models for the variance. A couple of different examples of how once you kind of put this in a probabilistic or Bayesian framework, you can kind of pull apart the data model from the process model. And it gives you more flexibility to tweak either, to better represent your data and your question. In this case, so the red is the linear model run as if the variance was constant. And the green is that second model run with changing variance over time. So x is here. These are our observations. And the true line is in the middle. Dark black, red and green fall on each other here. And here, green, or the one that actually models variance, actually the credible interval changes over time, right? Over x, not time. And the red doesn't really. So the mean of the posterior also better represents, better reflects the true mean once you actually take into account the variance. But there's no difference, really, if you do it on data that are not heteroscedastic. And then in this case, this is a model that Mike ran. But the DIC, the Deviance Information Criterion, which is one way you can evaluate a Bayesian model similar to an AIC, right? So what have we done here? We've added another parameter to the green. Okay, so it's got one more parameter. So in a traditional information criterion, right, that's a penalty. It's the same data, but now you have one more parameter. And when you have smaller numbers, that's actually a better fit. So even though you've added another parameter, you better fit the data and you've increased the information criterion. And so again, we're talking this week about uncertainty. Doing a better job at capturing uncertainty does not necessarily mean you're going to get more certain. You're more certain if you actually you're more certain out here, if you use the model that doesn't fit as well, right? That doesn't make you right, and that becomes much more problematic when you're trying to forecast. All right, so another kind of class of assumptions, the observation error. The regression model assumes all error is in the y's. Again, so you figure out what the best fit line is and you calculate residuals by looking at the difference between your observation and the line. There's always some uncertainty associated with the parameters that you estimate. You can make that uncertainty smaller by collecting more observations. But the noise that's left is always assumed to be in y. In the observation itself, if you had the perfect model, if your process model was perfect, then that's fine. It's important to know what your observation error is. Maybe really everything that describes that line in the real variability is in the model. The model explains 40% of the variance in that and maybe really 60% really is just, you can't work a DBH tape. But I don't think most people get to the end of their linear regression and feel that way. But usually there are a few that you think probably something else is biologically relevant and it's not just I couldn't count it. But that's what the assumption is, okay? So generally we assume that the observation error is symmetric about that mean or a predicted value. But sometimes you actually also don't measure x very well, right? So sometimes there's actually error in our predictor variables. And in fact, a lot of times there's probably error in our predictor variables. And there are few more frequentist opportunities for capturing that. Sometimes it's not a big deal and sometimes it could be. You can imagine that if there's error in x and you're trying to use x to make a prediction, then that error is going to propagate out and also be in your prediction. If you haven't somehow accounted for it or described it, then it makes your prediction overconfident. All right, so errors in variables is a way to deal with the fact that we can often have errors in x or uncertainty in our predictor variables, as well as in our response variables, okay? So classic assumption is that it's all in the response variables. But in ecology, the reality is that it's often also in our predictor variables. So how do you deal with that? And the Bayesian framework, again, because we have this probabilistic structure allows us flexibility to build that into a model. So in this case, what we've done is we've got the same kind of linear model we've been working with. Where our parameters describe the variance around a slope, for instance, in the intercept and the observation error. But also, we've got a model for the variable, the predictor variable x. That's described by its own set of parameters and also informs y. And I've written it up here, a little bit different. So we're actually modeling x as a random variable. Now we're going to move into latent variables. So anything that's not directly observed, sometimes that's just error. Sometimes that's variables are measured with error, whether that error is biased. Meaning you've got an instrument that's always a little bit high. Or it's actually got the random error, as assumed, generally with measurement error. And you want to account for that explicitly. If you have missing data, and you want to estimate data that you didn't actually collect. And more often, we hear about latent variables with proxy measures. So you've got something that you measure. And you've got something that you actually want to interpret. And I think we heard a lot of people in the project descriptions talk about, I've got these data, and this is what I want to do with these data. And generally, I want to summarize those data exactly because I think they're the true measurement of the population. That you've got data that represents some bigger population. And sometimes they're actually proxies for something that you can't count. But you could also, I think the book talks about GPP being a proxy.  And then you kind of put stuff together. And NEP would be a clear example. You put stuff together and try to make some summaries about net ecosystem productivity. So ignoring the fact that there are latent variables can have a whole bunch of outcomes, which is modeling a derived response or a flawed observation can lead to incorrect or falsely overconfident conclusions. I think everybody knows that. But if you go back and look through a lot of analyses, it does not stop most people from doing it. So we'll talk more explicitly about what is a latent variable and how might you treat it. So first, missing data. You've got a data set. This is something that has a response on the y. And some variable, this case I wrote time on the x. And you've got missing data. If you've got a good model, then you could predict what's missing here. You could predict y given some known x. And the error that you've already estimated. You can make good predictions if your model is a good representation of those data. That's what probably most people have experience doing as far as missing data. Or you use regression to fill in a gap. That's this example. I guess sometimes people fill them in other ways. But generally, people are more familiar with, you've got a missing y. I fill it in given my model. In a Bayesian framework, again, because it's probability and everything that's not known is being treated as a random variable, if you have missing data, when you run the model, it will estimate those missing y's. Likewise, if you have missing x's, missing predictor variables, and you have a model for those predictor variables, as long as you define a model for them, a distribution that it can draw from, then you can estimate the predictor variables. And not only can you estimate what they are, the missing ones, but you can estimate how they influence the y's. It does it all at once. So that's pretty powerful, especially if you have you've been out there collecting data, and you have lots of data, but then you've got weird sensor things, or somebody got shins splints and couldn't help you that day, so you've missed counts on one day. You've got random things that are missing throughout your data set. Most often in a linear regression, in a classic sense, you would have to just kind of get rid of that whole row. Or you would have to do some kind of gap filling that just fills in a point value. This allows you to actually estimate what it should be, as well as look at the influence of that missing data on the rest of the response or the response variables. So by the time you get down here, everything's been defined as to what distribution it was drawn from. But you're going to update the regression model. That was the mean equals beta naught plus beta 1. You're going to update the regression model based on all the rows of data given the current values of the missing data. You start the model with an initial value. Then you update it. And then you update the missing data based on that regression model, which is similar to a regression gap filling, except that you're doing it in this iterative fashion. So you're actually also not just gap filling a point. You're actually filling in with all of the uncertainty in how much information you have to inform that point. In order to do that, you assume that your data are missing at random. If your data are not missing at random, if there is some reason that you're missing chunks of data that happens over and over like we never got night. Or this one person, every time they went out, things went really badly. And they were out every Tuesday. I mean, if there are reasons that you have missing data, then you have to build a better missing data model than what I just showed. That's based on the idea that you just have random missing data. If it's systematic, then you can't estimate data that's missing if you don't have other data that should be replicates of it. I mean, this is to say, again, there's a lot of power in Bayesian inference and in using a probabilistic approach to statistics. But if you don't have information in the data, you're still not going to do magic with the statistics. Layton variables, when you observe y, but interpret it as z. And we already talked about the fact that sometimes we already know we do that with our data model. We're saying that we're observing data and expecting it to come from this observation error or this true distribution. But missing data and then these proxies where you have one or multiple things informing a y, informing your response variable, are more common. And then I was going to end with an example of the breadth of one Layton data approach. But so this is actually going back more than a decade for me when I was a forest ecologist. And I worked at the Duke Forest face site where they was kind of one of the coolest experiments that I've worked on because it was just so big. It was DOE, right? So they had these big, big things that kind of extended above the trees and just blew carbon dioxide onto them at the time at what was like a pretty futuristic CO2 level. And it was a pine plantation, so they're all the same age. So we know the year that the gas was turned on. We know how old all the pine trees were. They're all the same species. It's kind of like a jar of dirt that you put in an incubator but out in big scale. And I got to spend September and October climbing up those towers above the trees with binoculars and counting things. It was a beautiful PhD. Perfect time of the year. Everyone else in the lab is out in August measuring trees. They're all poison ivy infested and only go out in the fall. And so they'd been collecting seeds before I got there. They put out these laundry baskets and collected seeds in the baskets. And they could see that you got more seeds in the baskets in the elevated, which are the red or the bigger ones here, and fewer in the ambient rings. And there were three ambient and three elevated. And so the question here is this is a CO2 fumigation response. If you're not a forestry college, what you need to know is the fecundity of trees is often related to tree size. So you make the assumption that the bigger the tree is, the more resources it has to put into fecundity. They have to grow big first. And then they start putting out seeds. And then people do things like fecundity equals the number of seeds, which is a function of diameter and treatment. Tree size isn't really diameter, but it's a good proxy. Seed number is really hard to estimate. If you want to count them on a tree, it turns out that's hard. And so you count them in these seed baskets. And then you have to try to figure out how many of them came from what tree. So right away, we've got error in y, error in x. And then I'm trying to ask about this treatment response. But also, there's the question of, is this more trees putting out more seeds? So are there more trees contributing seeds? Or are the trees just bigger and contributing more seeds? So there's lots of different layers to this, which made it a good PhD. And when we went out and looked, so remember, the assumption is that if you know the diameter, you can predict something about fecundity. And we went out and looked, and this is total seed cones. So I counted cones because counting seeds is insane. And you could actually count the cones on a tree with binoculars. And then the diameter of the tree is here. And if I pull out one chunk here, you can see that there's a ton of variance in fecundity at that diameter, especially in the elevated. But if I moved it over here, then you would see that there's a ton of variance in the ambient as well. And so there's a lot of individual variability in seed production. If you just wanted to do a mean, then at that diameter class, the mean ambient would be about seven cones per tree. And the mean elevated is about 52. But you can see that that mean, it isn't really representative of these data, especially for elevated. And I'm not a perfect counter, but I'm not off by 50 either. So the variance there is more than just my inability to count. And so we built a model that could take a bunch of different information into that process of fecundity. So where the fecundity is still a function of tree size, it's now conditioned on whether or not the tree is mature. There's a couple of different ways you can get zero. You can get zero because the tree just didn't produce anything that year, which happens in some years. You can get zero because it's not mature. It shouldn't be counted as mature. There's a different trigger that seems to happen that turns a tree reproductive. And we used both the cones and the seeds to inform the fecundity estimate. And part of the reason we were able to do that is because we had older trees. Remember, all these trees are the same age. And so even though they kind of have a diameter range that goes from probably around 10 to up to about 30, that's not really a big diameter range in terms of trees or even loblolly pine. And we actually had information about bigger trees that could help. Remember, if you don't have any information out here about 40 centimeter trees, then your line here, when you're trying to estimate a change in fecundity with size, can't really inform that very well. And so if you have information to constrict what that should look like, which we did with these other seed traps in other stands, then we could actually use that to help constrain what's observation error from what's actually something within the realm of natural variability. So you can use different data sources to start to describe a latent variable. And in this case, we've got the latent variable of maturation status and fecundity, which is both a function of information derived from numbers of seeds and laundry baskets that comes in via this dispersal model and the number of cones that were counted on a tree.


Hey guys! Hello! Hello! Today we're cooking Mother's Day brunch. You're really chimed along. Ready? Yeah! Are we ready? Yeah! Today we are making Mother's Day brunch in under 20 minutes. It's super easy. Your kids can basically do the whole thing. There's no cooking involved. You might even be able to sneak in a cocktail while it's all going on. It's yummy. Emma says it's yummy. I'm hungry. The first thing we're doing is we're going to make bacon. You just lay the bacon out on a sheet tray. You stick it in the oven. Cold oven at 400 degrees 15 to 20 minutes. It's done every time. It's also the easiest bacon to clean up. What's your idea? Lay it on my arm. All right. I think we're good. I think we can't put it fit any more bacon on the top. Yeah. I think we'll save this for tomorrow. We can always save this part for tomorrow. Because tomorrow has plenty of bacon. I'm going to go stick this in the oven and then we'll move on to the pancakes. I'm going to wash my hands. Bacon's in the oven and now we're going to start assembling our brunch. It's going to involve microwave pancakes and cookie cutters. It's super easy. Microwave pancakes are delicious. You've got cookie cutters. You can make delicious shapes. Jake is going to cut up some fruit for our pancake board. I'm going to have this little one. Okay. Do you know what you have to do? Yeah. That's okay. It's okay. It's not 100%. I'm going to go over here and grab a board that we're going to put all this stuff on. So we have to start putting the pancakes on. Oh, it's beautiful. Look at this. Here is a quick tip. If you just put a little bit of cooking spray on these cookie cutters, they will slide right in and out of the pancakes and they don't affect the flavor at all. I like to make. Let us know in the comments. Keep cutting out shapes.  Good job. All right. So we are also going to throw together some really quick yogurt parfaits, yogurt, granola, fruits, the fruits we already cut up for our pancake board and then just because to taste good. To taste good. That's right. That's exactly why. The only requirement is for every M&M you put in, you have to put in a piece of fruit. He said a weed cut. I don't know, 17 minutes or so and I'm going to take this bacon out of oven. All right. Bacon is out of the oven. Parfaits are done. Fruit is cut. Pancakes are cut. We're just going to assemble this and then the surprise will be ready. Here we go. Let's put Oh God. This is hard to move. Who likes syrup on their pancakes? Who would like syrup on their bacon? Me. And then that looks good to me. Some heart pancakes. Really cool. One of the ninjas. Oh, and a ninja. I love it. I am responsible for the Bloody Mary. Thank you.


Myself Sunil Rathi, I am working with Swiggy as a senior data scientist. Today we are going to discuss predicting order batchability. So this is the flow of the presentation. We will start with the important terms. These are important to be aligned with the presentation. Then we will jump to the introduction, batching, traditional order flow, what should be the next version of order flow, machine learning batching prediction algorithms, first order and second order, then our machine learning model and then the experiment, batching. So batching simply means to deliver two or more than two orders in a single trip by a single delivery executive. I think you all must have experienced, Uber, Pura, all us here is a similar concept. First order, say you have n orders in your batch. If you sort all the orders by the order time, then the order which have the minimum order time will be the first order and all the rest of the orders we will call as second order. Predictive service level agreement or PSLA. So this is the estimated time which conveyed to the customer that how much time this delivery will take. Service level agreement or SLA is the actual time taken by the delivery to deliver the food. Compliant orders. Compliant orders means that where we meet our promises, basically where the actual SLA is less than or equals to the predicted SLA. Net promoter score or NPS is a business metric that we generally use to track how the business is going on. This is from the customer feedback. So before we start the presentation, let's first discuss why do we even need it. Any, every company is doing it, patching any, all a share is doing, Uber is doing and rest of the companies are also doing it. But why do we need this prediction? I will tell you a short story so that you will be better aligned with the presentation that why are we doing it. This big why. So I have a friend, Jagmeet, who generally orders food from Swiggy. As usual one day, he was hungry. He went to the Swiggy platform. He went to his favorite restaurant. He picked up the items, added it to the cart. At cart we showed the predicted SLA that we discussed. He was happy with it. We showed it 33 minutes. He placed the orders by looking at that. This is 33 minutes. Okay, I'm fine with it. Everything was fine. But the order was in peak hours and that too in dinner peak. At this time, we have a lot of orders. So to handle load at peak hours, we generally batch the orders. Such that they made the necessary conditions and customer experience doesn't hamper. So Jagmeet's order got batched with order O2, say from customer C2. Now the delivery executive has to wait extra to pick up the second order. He went ahead to deliver the C2 food first rather than the Jagmeet's food. And then he moved ahead and then he delivered to the Jagmeet. So overall in all these operations, it took something around 46 minutes. We promised 33 minutes. We were unable to meet our promise. We felt bad. We apologized to the customer. We took all the actions which we generally take when we don't meet the promises. Next day, we were sitting together as a data science team. We were brainstorming that why are we not able to meet the promise? Then we looked at the data for the batched orders and non-bashed orders and we find that this is happening more frequently with batched orders if you compare with the non-bashed orders. We asked the questions within the team, why are we not able to meet the promise? So there was a simple answer because at the time of prediction we don't know that this order is going to batch in future. Fair answer. Right answer. But is it a solution? No. Let's go through that how we build the solution. So let's compare a normal load scenario. So here we have NFDs in comparison to the number of orders. It's a normal load. So delivery executive can pick up a single order and he can deliver it to the customer. But what will happen at high load? At high load, there will be delivery executives who are busy in delivering the food. So these two guys are busy. Now just consider a simple scenario that there are two orders. So let's first compare with the... So we know the pigeonhole principle, right? So it says that if you have M buckets and N items and number of items are greater than number of buckets then some buckets will have more than one item. Same here. We have less number of delivery executives in comparison to the number of orders. So some delivery executives has to deliver more than one order in a single trip. And that is batching. So this delivery guy has to pick up the orders from R1 and R2 restaurant and then he has to deliver it to the customer C1 and C2. So we discussed what is batching. But why are we doing it? There should be some perks. So we discussed that it handles load at the peak hours. It gives a better utilization and payout to the delivery executive. So if the order are best, the simplest scenario there are two orders in a batch and in a general case if you are paying something like 50 rupees then for the same trip he is getting extra money with the cost of extra time, little extra time that he has to move from customer C1 to customer C2 and if it's a multi-restaurant then R1 to R2 also. It's a small time in comparison to total trip. Now from the business side it reduces cost for delivery. So if the orders are best, so say for first order you are paying 50 rupees and you said that if the order gets batched with this order you will pay something like 30 rupees or 40 rupees. Say 40 rupees. Now per delivery if the order is best then you are saving 10 rupees. So it's a win-win situation for both business as well as delivery executives. It increases speed for second order. So at high load what is the problem that we have less number of delivery executives in comparison to the number of orders. So if we don't do batching then the order will wait at the restaurant until the delivery executives deliver the food to the customer and then come back. It took a lot of time. If you just batch it then he will just pick it up with the cost of the preparation time of the second order that can be small. So overall it increases the speed of the second order. So let's look at the traditional order flow. So when you choose some items and add it to the cart you are at cart where we predict the SLA. We predict the delivery time. So now if the customer is happy he will place the order. Now when this order comes to our environment we'll check that whether this order can be best with some existing order or not. If it is then we will assign a same delivery executive as we will assign a new delivery executive and then it follows the rest of the steps. So if you focus closely this prediction of the SLA is before batching same Jagmeet story we discussed that the answer was simple that we don't know at the time of prediction that this order is going to batch. So now look at the numbers that will tell you that where are we going wrong. After deploying the batching thing after few weeks we compared the numbers between batch orders and non-batch orders. So the compliance of batch orders was 20% less than the non-batch orders. Batching was a major contributor to the bad orders.  There were higher chances of customer to connect to the customer care because you are not able to meet your promise just after the estimated time they will connect to the customer care to ask where is my order. So we looked at the numbers how it can impact the business. First thing is repeat. If you are not able to meet your promise then the customer will not be happy and he or she will not order food as frequently as a happy customer. So the repeat rate can change. It can decrease and pierce. So when we compared the numbers between batch and non-batch orders there was a 16 point difference between these. So with all the perks of batching still the business was going in the negative direction because of the customer experience because we are not able to meet the promises. Customer care. If you keep on breaking your promises there can come a point at which the customer can look for the other platform. You can lose your loyal customers. So in a polite way I will just say that we should respect our promises. So we looked at the previous version of the order flow and we found that there are some issues even with the perks of batching. So how should we do it? Now let's look it. So when we add the cart we will check whether this current order can be batched with existing order or not. If it is then it becomes a second order. So second order batching prediction will handle it. This prediction thing. If it is then we know the environment with which order it is going to batch and then we can predict the right asset. If it doesn't batch with the existing order then it becomes a first order and here comes the role of first order batching prediction or core machine learning algorithm. It will predict that whether the future batching is possible for this order or not. Because there is no other order in the current system right now that this can batch. Is future batching possible or not? If it is then we will recalculate the SLA. Else we will throw the same SLA as predicted by our time prediction algorithm. So in the context of batching predictions we handle differently the second order in the first order. Because they have different behaviors. Because second order we already know the cause with which order it will be batched with first order we don't know. That is the second order batching prediction because that is much more trivial than the first order batching prediction. Second order batching prediction. So when the order is at the cart you have all the information about your environment all the orders all the batches. Now with this current order you can check whether this order can be best with existing order or not by turning all your conditions. If it can be best then you know that with which order this will be best. At this point you know all the information that what will be the customer to customer distance what will be the restaurant to restaurant distance what will be the wait time so all these properties. So you can use these information you will pass this information to your time prediction algorithms and you can calculate the right SLA. If it can't be best with the existing order then you will throw it to the first order batching prediction. First order batching prediction. So now the order didn't batch with the existing order it can batch with some future order. So how will you identify that there can be a possible future order that will meet all the necessary conditions and it will batch with this order. So first order batching prediction mainly depends upon demand prediction batching conditions order level predictions and historical features we will go through one by one. Before that let's discuss that what is geo hash because some of these some features are dependent on this. So we will quickly go through geo hash. So geo hash is a public domain geo coding system. What it does is that taking a space it will divide into grids and keep on dividing into it so that you will get higher precision. So these are some of the precisions to which you can work on. So we are basically working on the precision 6 that is 6, 10 by 6, 10 meter for the first version of our first order batching prediction algorithm. So geo hash is just a string the higher the length of the string the precision or smaller the size demand prediction. We have an order at card. We want to identify that can there be a second order that can be batched with this order. So we are now depending upon future we have to predict the future. So from the demand perspective demand prediction perspective we need to find restaurant future order. So this order comes at our restaurant R1 can there be a future orders for this R1 so our demand prediction algorithm will tell you that say that this order comes at time T between this time T to T plus delta T can there be future orders? If there are good number of future orders then there are higher chances of batching. So this delta T you have to set according to customer experience you have to look at the data. This can be 10 minutes, 15 minutes it depends on the predicted SLA of the order also. If it is only 10 minutes you can say that for this order we can deliver it in 25 minutes 15 minutes something like that. Same for customer geo hash. So if you can identify that there will be good number of future orders you can find the order density for that particular geo hash. Now we already discussed that we are working on the geo hash of 6 10 by 6 10 meters. So if the order comes from that geo hash then it can meet the one of the necessary condition that customer to customer distance. We will discuss about it what is customer to customer distance. Then customer geo hash to restaurant future orders. So from particular geo hash to particular restaurant what will be the future orders? So which is the good number? Now customer to customer distance will be small restaurant to restaurant distance will also be small so there will be high chances of batching batching conditions. So these are basically put up by the business or operations such that we have a better customer experience. So it can vary from geography to geography. So what can work in say Bangalore will not work in Ambala and vice-versa. So major contributions are from customer to customer distance. So customer to customer distance is that how far to two customers will be such that their orders can be best. So if you say that customer to customer distance is one kilometer that means two customers will be can be at my at most one kilometer max order delivery time. So this is the maximum time that should be taken by a batch by all the orders in that batch. So if you set say something like 55 minutes then all the orders should be best in this 55 minutes. Sorry should be delivered in 55 minutes. This makes all delivery time customer to customer. You have to look at the data that what is the sweet spot? What uses you should set it should be different for the high load. It should be different for the normal load. So it can normally you can choose something like where the customer experience is right. So you can choose something like 45 minutes then there are item level conditions. This is basically like for some particular some items can't be best if you take an example of ice cream if you bet that then it can be meltdown for these particular items you have to treat differently order level predictions. So now we know that we have a current order at the cart. For this order we can find the necessary properties like what will the prep time preparation time what will be the predict SLA. So by using all this information we can identify some of the batching properties because this is part of the batch. Major contribution is something from order preparation time. So same example if it's ice cream you just give you a scoop and then you can have it. So its preparation time is less than a minute. So within this time window within this minute there are less chances of batching because the future orders will not be much. Order service level agreement that is predicted SLA. So if you set the maximum delivery time say at 55 minutes if this order is PSLA something around say 52 minutes then there are less chances because if you batch another order there are chances that you will not meet the necessary condition. Part of it is item level constraints. So these are again basically specifically for the items like it can be best only with pizza and something like that. Ice cream can't be best. Ice cream can only be best with the ice cream or it should be become the second order then only it can be best. So these are some of the conditions. Historical features. History tells you about future. So if you can look at the history you can identify that what was going on so that you can predict something about the future. So restaurant batching ratio. So if you know that a particular restaurant is popular in this order bucket for this particular time interval by looking at the some few weeks back or some days back or months back then you can some guess that this restaurant can have a high batching ratio at this time. If you just compare that for biryani people generally orders at the dinner peaks. So restaurants who are serving biryanis have high restaurant batching ratio for that dinner peaks. Same for customer g-hash for customer g-hash batching ratio you can take an example. So say you had a customer lead long from that you can find the g-hash. Now if this g-hash is falling into office complex area then there are higher chances that this will be best in lunch peaks rather than the dinner peaks because they will move to the residential areas to their home and customer past experience. We checked that what was the experience of the customer and the previous five or ten deliveries. If we were not able to meet the promises then the our batching prediction algorithm will handle it by saying that you shouldn't bear this. Assign a new delivery executor. Let's give a customer a really good experience. So this slide is specifically for data scientists. So we experimented with different models random fog boostings. We come up with the deep learning model. So the highlights of the model are basically like we had the data points of around 11 million features used was something on 58 which we already discussed and some extra features from our platform. Number layers used was 8. We used value as activation function for hidden layers. Adam as optimized and the loss function was binary cross entropy. We checked the results and the results was good. So overall for the first version it was precision 90% recall 84% and F1 score 86%. So we were happy with the results. We deployed this model to some of the zones in Hyderabad and for this version we didn't do any change from the time prediction algorithm side. What we do is that say your time prediction algorithm for this current order is predicting say 30 minutes. Now your batching prediction algorithm is also saying that this order can be batched in future. So we'll just increase this by 10 minutes. So in spite of saying 30 minutes we will say to the customer that it will take 40 minutes. We integrated this model using a data science platform with our internal data science platform. So the initial question from the business side was now you are increasing 10 minutes at the cart. So there are chances that there will be conversion drop. We told them that we are not increasing 10 minutes for every order. These are the highly probable orders that can be batched in future that we have to increase 10 minutes because we will not be able to meet promise. This will be a small percentage. They gave us a go ahead. We deployed in Hyderabad some zones. We found that there was no drop in conversion because people can understand that this is a dinner peak. This is a lunch peak. It generally takes something this much time. Even when we were saying that it will take less time, they didn't know that it will take a lot of time. So when we are promising that there was no conversion drop. Compliance for batched orders improved by 15% because of better promises now. In spite of PSLA for these batched orders, we are saying PSLA plus 10. The NPS and customer chain exercises are going on and we expect the results in some few weeks when we get it we can share with the community. We already have a second version of this model which have better results and we are expecting that this can give us a jump of 10% additionally. We will deploy this in one or two weeks next. So that's all from my side. I'm open for questions. I think no one sleeps. How do you gather the data on like restaurant delivery and food prep behavior? So item wise. So writer is not item wise. It's on the order wise. So we get the items as a feature for our order and we get the preparation time for the order. So we have the in-house machine learning algorithms that predict these preparation time. We already have the data, right? So we have the prep signal, mark for ready from the restaurant side. So we know that how much time it took from the history. Now we can use this data to predict that what will happen in the future.  This data is going to add into our database and then we are using it. So for this new restaurant, we have the cuisine properties like so for this restaurant, it will fall into one of the cluster and from that we can identify it. They want this for you. You have those 30 minutes and free orders, right? 30 minutes of free orders. So I ordered once. I felt like it came in like 31, 32 minutes. And I got a notification, your order was delivered within the 30 minutes. So next time I timed it. It's on the reach, not on the delivery. What is that? So say if you are living in a complex, then the first entry gate. But I'm living in an independent house. Then it's still with you. And next time I started a stopwatch, it came in 31 minutes and it was still not free. We can look into your issue. Actually, I'm at the other end of the spectrum. So I ordered a 30 million delivery once and I got it free both times. We'll also look into you. We are mentioning about giving degraded SLA, slightly degraded SLA for places where you kind of have to batch the orders. You told me that you did not see any drop off in terms of the customers. And what's the drop? Okay, the slight drop. But did you try incentivizing those customers to probably... We are thinking about this experiment that if we know that this order can be batched in future, we will give some incentive that some discount. You haven't tried it out yet. We are doing this experiment. It's on the POC. Hi, one question here. You were talking about increasing the promise time to your customers for orders that you are planning to batch. Have you considered the possibility of offering it as a new service offering for customers, wherein I would... For orders that you're willing to wait longer, I would leave a lesser delivery charge, no surge and so on. So that exercise also we are doing it. In couple of months, you will see that we will get some incentives to the customer who was ready for say one hour delivery or something like that. Yeah, hi. One more question. A key component of your SLA also involves the restaurant keeping their part of the deal that they also meet their SLA. But they might be on multiple platforms. They might have their own customers who just walked into the restaurant. And if the number of chefs, for example, for that day is less, so how do you account for that? Operations guys keep on discussing with these restaurants to tell them that if you get the signal, you should admire the prep time which we are proposing to you. So we already discussed that Mark could try to do some kind of thing. So we actually penalized the restaurants which didn't follow our instructions. On the listing side you can see. Hi. So I wanted to ask a question about the machine learning aspect of your model. So you talked about the model parameters. I wanted to ask what kind of models did you try and how did you arrive to the current model that this is the best that works for you? So we experimented with random forest boosting algorithm, deep learning model. As we have a lot of data, we have collected for say around three months. Deep learning model was working fine with a good set of parameters. The parameters also we went in with different set of parameters.


Welcome to Fairwald Park, where we have transformed the entire park to a much more community-friendly park. This project was made possible through a community development block grant, and it allowed us to really give the community a special space. Our contractor for the park was Great Southern Recreation, and they did an outstanding job implementing the vision. Before work began, the park had an assortment of outdated play equipment and was surrounded by a high chain-link fence. The revitalized park features a new playground, walk-in path, outdoor gym stations, shade shelter, open green space, established tree canopy, side amenities, and landscaping with a new split rail fence. You enter the park at the park gateway, where we have landscaping that blooms throughout the year and new split rail fencing, which replaces the old chain-link fence and is much more inviting. The gateway connects to the walk-in path and adjacent open space. The open space was created by selectively removing a few large pine trees to provide the adequate sunlight and proper soil conditions for a grassy open space. As you can also see, there are plenty of trees that we also preserved in the park. As you continue on the path, we have a large playground that features an assortment of play elements. The playground is a post and platform system with a variety of play components, such as climbing, slides, bridges, tunnels, and swings for plenty play adventure for 5-12 year olds in this area. The surfacing is engineered wood fiber, which is accessible and provides the proper fall safety for the playground. It also has a seamless transition from the playground to the walkway. The next playground is our 2-5 year old area and features an assortment of climbing slides and play adventure with a nature-based design theme. As you can see, we have a log, valance beam, and boulders to climb on, a climbing net, tree climbers to give you that nature-based feel. The surfacing is engineered wood fiber with a seamless connection to the sidewalk, and again this is for 2-5 year olds. The outdoor gym stations begin along our path and have 6 different stations. The first station is a two person back and arms combo. The next station is for pendulum, abs, and a dip station. The third station is a combo lat pull down and vertical press. The fourth station is a four person leg press. The fifth station is a rowing machine. And the final station is a two person chest press. The park has seating along the path at select locations and a shade shelter which fears out into the open space for community events and picnics. The new park here in Fairwald is a community driven design with a mix of passive and active uses and we hope you can visit the park to experience for yourself soon.


Thank you Mayor Brooks. In accordance with California Senate Bill 361, this meeting is not physically open to the public. Council and staff are meeting via Zoom and there are several ways for the public to watch and participate. Information on how to join the meeting using Zoom or a landline mobile phone, along with how to submit public comment during the meeting tonight, is available on our website cityofcapitola.org and on the published meeting agenda. The public can also live stream the meeting on the city's website. As always this meeting is cablecast live on Charter Communications Cable TV channel 8 and is being recorded to be rebroadcast on the following Wednesday at 8 a.m. and on Saturday following the first rebroadcast at 1 p.m. on Charter Channel 71 and Comcast Channel 25. Our technician tonight is Walter. Thank you Walter and thank you Mayor Brooks. Chloe and thank you Walter. You can also join me in the Pledge of Allegiance. I pledge allegiance to the flag of the United States of America and to the republic for which it stands, one nation under God, indivisible, with liberty and justice for all. Thank you. All right we're now going to move to item two, presentations for our police policy review. And shake down me, this is your item. Excellent. Okay I'm going to start by sharing my screen. Okay can you hear me and see my screen? We can. Awesome. Okay well good evening Mayor Brooks. I count the members and city staff. Tonight I have the pleasure of presenting a couple topics. The first item is the 2021 Jantree's County Criminal Justice Council Report. And the second is to provide an update on a new program that the Department's really excited about and that's in the Chiefs Advisor Committee that will be coming in 2022. So let me start by beginning to talk about the Criminal Justice Council of Santa Cruz County. And okay so I'll start with, what is the CJP? And really let me start by highlighting the great work of the Criminal Justice Council of Santa Cruz. It was started or formed 30 years ago as mayor of high level public partners from a wide spectrum of expertise and diversity throughout Santa Cruz County. And the CJP was created to increase the coordination between the Criminal Justice Partners, nonprofits, educational leaders, and other members. The membership of the CJP includes several board supervisors, same council members, the Sheriff of Santa Cruz County Sheriff for several police chiefs, probation, Santa Cruz County Courts, members of Santa Cruz County Courts, we have the District Attorney, Defense Council, School of Executives, and quite a few other non-profit agencies. So this group is they have a very competent public leaders looking at criminal justice in Santa Cruz County. And each year the Criminal Justice Council selects a topic and for 2021 following the death of George Floyd in the national discussions around police policies the CJP appropriately focused on a regional review and analysis of the public safety policies for the police departments in Santa Cruz County. And so the CJP formed a subcommittee that was led by Supervisor Zach Friend and Council Member Justin Cummings. And so they have a subcommittee and then we have representatives from the City of Capitola which included Council Member Kristen Peterson and then Chief McManus. The CJP reviewed local police policies and focused on use of force, the technology, independent oversight, behavioral health response, and community transparency. And I mentioned exactly what they were looking for. And so the purpose was the CJP really wanted to examine local police policies to determine three things. The first thing was whether or not they were consistent and had alignment throughout the county. So Santa Cruz County is a small county that people can transition between jurisdictions. So just seeing that there's there's consistency among the police agencies as someone traverses through the county and they have an interaction with law enforcement. And the second thing is with trying to identify if there's anything in gaps that need to be addressed. And then finally if there's any opportunities to improve. And so with that the CJP did make a very note worth the observation that all the law enforcement agencies participated or are very open and transparent during the process. So the next slide I'm just going to highlight capitol police department because they give this touch to all the different law enforcement agencies in Santa Cruz County. All of the different departments you can find these links on the CJP website. So this is what you can find on the CJP website and it talks about the different some of the some of the highlight that you have in front of you. And on the left you'll see the police topic and on the right you'll see the actually the actual the policy that you can find all of our policies are online that's mandated by law and all the Santa Cruz County agencies have those online accessible to the public. For the policies that you do see that have a no-policy I feel quite honestly for those that you don't have policies that's just like a no-knock from no-knock or the dynamic industry. It's because we don't have those policies primarily found like swap teams and tactical tactical teams and we just don't have those with our police department. The no-knock warrants are justified by law but again it has to have a policy with some sort of tactical team behind it which we just don't have. Other agencies do regulate actions of directives or from the chief of police for like high research months and policies such as those. The DCGP did report did give us the opportunity to explore ways that we can improve. Sorry and then he just jumped into what the kind of big key takeaways were from the DCGP report is that there was widespread policy alignment in Santa Cruz County so that was a very very positive report. And then all of the Santa Cruz County law enforcement agencies have updated and modern policies regarding use of force technology deployment and release of information to the public. And among the agencies that don't have a dedicated unit for behavioral health that they were all interested in and created agencies that have responded to those types of calls. And then one of the things that two of my friends noted is that legally that this is actually Santa Cruz County believe it to be the first to conduct this type of transparent regional policy review in the United States. And then the DCGP did want to point out that this report is not a comprehensive look at both ways to do policies but it really provides more of a transparent overview of the policies that were to ensure that officers respond safely and responsible to the key situation. And with that I know that you do have a pathway and the package and that is that review is publicly available so it was a really really really great effort for the DCGP and with that we kind of spun into a little update now on the what's next the next steps for Capitol PD as we kind of approach 2022. And so with that for 2022 what my goal is to create this chief advisory committee and what it does is it creates a link between the community police department as it's used as a resource for the chief of police. And it's a mechanism for the community stakeholders to understand community relations, understand police policies and procedures, learn about police initiatives and then make suggestions towards our capital police department operations as we look towards following advice issues for our residents. And some of those examples can include program review and community outreach assistance. So on this question who would make up this type of committee? The composition of the committee with within my vision is they would have five to seventy two stakeholders who want to keep the group manageable and they need to be active community members. I want to represent a very diverse group of stakeholders within the community that can make this recommendation. And some examples and this is just kind of arranging with the interests of the community leaders, business owners, education leaders, public relations, the state of commanding and youth representation. All right so what are the next steps for 2022? You know the capital police department are really committed to this to this program it was something that Chief McManus and I had discussed and one of the things we did is we kind of pulled this into some of our internal promotional processes and so we used it as one of our sergeant's tests written exam and actually Captain Ryan was working on an SLI project that has to do with the Chief's advisory board. So we can get that kind of internal buy-in and see that come from our agency. So the goal is to have a draft program by the end of 2021 and to finalize a policy by early 2022 looking at membership applications by the end of June of 2022 and then creation of the actual committee by the fiscal year of July 2022. And then let's see as I kind of wrap up here I just want to extend a major thanks to primarily Mayor Brooks for early conversations of these very very important topics about police policies and looking at the ATAC way that was kind of those first kind of first conversations that we had internally. And then more importantly or not more importantly but equally as important is just that really the time and effort that Captain Edward Peterson and Chief McManus put in with the excellent work that they did for public safety certain on the ATAC, the TJP community. And with that I really appreciate the time and time for listening and we'll operate because I don't think I can see I just see my slide shift. So with that I'm going to stop sharing so I can see everyone and I think everyone's back. You should have all been frowning right when you came back, right? Just to really show our dismay. Now great first presentation Chief Daly. I know it's not easy so especially talking to your screen. Council members, Vice Mayor's story, I'm a reflection. Vice Mayor's story, perfect. Thank you Mayor Brooks and congratulations once again Chief Daly. And I applaud your efforts to start up a community advisory committee. I look forward to seeing how that develops. But I wanted to particularly ask about one of the survey questions among the various jurisdictions was about having a dedicated staff or unit for behavioral and mental health issues. And I noticed we identified of course not having that but being able to benefit from that and supportive of maybe starting out I don't know maybe a multi-jurisdictional unit to respond to those types of calls. I was wondering if you had any thoughts about our field of discussions about how we may move forward and you know being a small jurisdiction whether there are any you know a feasible and affordable living for us to be able to have such a dedicated unit. Sure and great question and a very valid topic right now. So as far as the resources go the mental health workers that are out on the field right now with the unit for personnel they're out there they're not out in our building per se but we do have they are available to us. So the Santa Cruz Police Department has no health workers and the Sheriff's Department has no health workers that are available to us when they're working. So depending on the call for service we have the ability to request those resources and they're readily available. I will note that the CJT is actually I believe taking on really some of the well I'm sorry CJT is going to take on the home of this issue next I believe next year but the the topic of mental health and law enforcement and that partnership is something that's ongoing with all of us and we're all very interested in it. I really think that there's going to be some some funding that's going to be coming towards us and we would absolutely welcome any partnership with no health workers and to help our our unit for personnel absolutely. Well thank you that that actually seems kind of on the thing so and I would certainly want to you know support us to move forward in that effort and seeing if we can collaborate with the county mental health in order to get that kind of first response and and to support you know our police force. But thank you for bringing that to our attention and moving that forward. Appreciate it. Council Member, oh any other? Council, Council? Okay there's no other comments again. Thank you for the presentation Chief Derry. I have a good night and it just disappeared like that. There we go. Okay I'd like to item three report out on closed sessions. The evening Mayor and Council Members, we had two closed sessions on the agenda tonight. In one of the closed sessions regarding the matter of in-ray national prescription opioid litigation, Council voted to join the settlement agreement and operate with City Manager to take all necessary actions to effectuate Council's position. Thank you. Thank you so much Samantha. We're now going to move on to item four for additional materials to be having this evening. Yes Mayor Brooks. There were three public comment emails received regarding item 9A. That's the outdoor dining ordinance item. Thank you. Thank you Chloe. For item five additions and deletions to the agenda. Staff has no changes to the agenda tonight. Thank you Jamie. Item six, oral communications. This allows time for members of the public to address the City Council on any consent item on tonight's agenda or any topic within the jurisdiction of the City that is not on general governance. Do we have any comments from the public? Mayor Brooks, I do not see any emails on this item and I don't see any hand raised to speak to this item. Oh wait, I'm sorry. I do now see someone and this is general governance. Correct. I mean this is a consent. Yeah so again this is just a reminder for those of the public who are asking to speak. This is an item not on tonight's agenda or in reference to an item on consent. You will have a brief dog deli. Okay go ahead and unmute yourself. Yeah we just passed through the outdoor dining so I just wanted to make sure that we were going to cover those items on sidewalk seeing. Yeah so that's going to be in a few and a few more items we did not talk about. Okay sorry that sorry. It's all state.  Awesome thanks so much. Okay I know this stuff gets tricky folks. Okay so item seven, this is for staff and city council comments staff. Do you have any comments? First I just want to announce everyone is eligible for booster shots if you have been your six months or more since you got your Moderna or Pfizer vaccine or two months or more since the Johnson and Johnson. I just went and got by this afternoon of the Cabrillo drive-through clinic that they have there. Just want to encourage everyone to see that. And then our public works director, Steve, he has a little update for you. Meeting man council I just want to give you a quick update on the two ongoing construction projects we have. First is the war project that is requesting the files, the seal files that the head of the work and statewide's instruction under the restaurant. There are we two of the project. They have completed replacement of four with six piling on the head of the work. They anticipate having all the files replaced by the end of next week and which point they will move to stabilize the structure under the restaurant. At this time they are on schedule to complete the work by Christmas time so that's their role is to get out of there for a holiday. Regarding the signal project on 41st Avenue I just want to give everybody heads up with the contractor will be installing conduits across from one side of 41st to the other at three of the intersections within the city. They're going to try starting early in the morning at working through the new time hour and see how that works with traffic control. So there will be some congestion there with that. If that doesn't work they will consider moving the work into the next time. So we're kind of seeing what works. They're going to be closing a lane at a time as they go across but they will have moving traffic control. They're still waiting for equipment to come in regarding the actual installation of the controllers at each cabinet including the conference cabinet. So it's very likely that the actual work is the installation of the equipment will not begin until the new year. We just take the project being done in February and then the system may have to take track counts for about six weeks. Right now it's three weeks before they turn on the system. So we're looking at March before we do that. Probably see the benefits of the project. That's all. Thank you very much. Thanks so much Steve. Okay any council comments? Okay seeing no council comments they just had one. Three CE is our energy source. You usually see the name on your teaching e-bill on their website. They have just posted a reimbursement program for those who have bought a e-vehicle and now even e-bikes. There's a really large incentive anywhere from five hundred dollars to four thousand dollars. So I highly encourage people who have bought one or who might be thinking about buying one to participate in that program. So again that's three CE or Essential Coast Community Power program and you can google that at any time. All right moving on to item eight. This is consent. All items listed on consent will be enacted by one motion in the form listed below. So that's item 8A through F and would any of our council members like to entertain a motion. I can move it. I'll second. We have a first from council member Kaiser and a second from my vice mayor story to approve items 8A through F may have a roll call for you. Council member Bertrand. I approve. Council member Kaiser. I. Council member Peterson. I. Vice mayor story. I. Mayor Brooks. Thank you. We're moving right along our excuse me that I don't have to unanimously moving right along to item 98. This is outdoor dining ordinance recommended action item one to consider the planning commission recommendations regarding the draft outdoor dining ordinance and either and I'm going to pause right there. Council member Kaiser. Thank you mayor Brooks because of my information from each girl that pertains in the outdoor dining in my financial conflict. I will have to refuse from this item. Thank you. Thank you council member Kaiser. So I'll just go ahead and continue to read this is the outdoor dining ordinance separates from our temporary ordinance that we have in place. So this is considered planning commission recommendations regarding the draft outdoor dining ordinance and provide direction on this ordinance tonight and consider schedules for a first reading and adoption or introduced by title only waiting for the reading of the tax and ordinance of the city council of the city of capital repealing and replacing the municipal code as listed and amending it related to outdoor dining in the public right of place. So that'd be item one and then item two is to consider options to extend terminate or modify the temporary outdoor dining program currently scheduled to end on January 3rd. So we'll take both of those items separately just as an FYI because one does trail the other. Katie is this your item? Yes. Thank you mayor Brooks and council. Just as mayor Brooks explained I'm going to start with an overview of the outdoor dining ordinance. This is a long term program for the city of capital and it's looking at creating new regulations for up to three year program that could eventually be extended into a permanent program. And then following the discussion on that ordinance we'll talk about our COVID-19 temporary dining and what the council would like to do in terms of extending the program or letting it expire on the student expired January 3rd 2021. So thank you and I'll jump into the draft ordinance. Thank you. Okay there we go. So just a quick overview of where we've done. So back in spring of 2020 the city council issued an emergency temporary outdoor dining permit throughout the city of capital and not only it was in the village then in April of 2021 after almost a year of the outdoor dining city council directed staff to develop a permanent outdoor dining program. We came back on two more occasions to the city council the first time in June and brought forth two surveys one that was finding the outreach to the public and whether or not there was support for the program and there was a majority of support from the public in creating a permanent program and then also the surveys from the restaurants and again there was support for a permanent program. So with that we began drafting an ordinance we also um so began drafting an ordinance it was put out for public review in late September and October 7th the planning commission reviewed the draft ordinance for the first time. Today at that hearing the public noticing requirement is really just to put the public noticing in the paper. Planning commission asked us to continue the item give us some feedback on the draft. They asked us to continue the item and put out notices throughout the village and also to send a little green card to all residents within the central village. So it was continued to November 4th hearing and the planning commission went through the ordinance and gave us feedback on all of the items and directions but ultimately their recommendation was to delay the adoption of the ordinance until a prototype design is complete and I'll go into that for you. So there they're when they reviewed the ordinance there were three significant changes that occurred. The first was to remove sidewalk dining altogether. The second was to modify the locations in the street dining to help show you more detail of that and then also in terms of timing they asked to delay the ordinance until a prototype design could be in place and the reason for asking the prototype design to be in place first was to ensure the best design fits the ordinance. They had concerns about angle parking and as well as to ensure the on-site bicycle parking requirement which fit with the design. However in their recommendations of a lot of the issues to the ordinance there is now an in-law option for the bicycle parking requirement. So with the differences between the original city council direction on what to include in the ordinance and the planning commission's recommendation being having three significant changes from the original direction from city council. We brought forth tonight two ordinances. One ordinance is the planning commission recommendation of ordinance one and then the second ordinance is planning commission recommendation but it includes city council's original direction on locations for sidewalk and street dining. On this slide I just brought up the differences of those three items. So for sidewalk dining the planning commission said to remove all sidewalk dining from the ordinance. Within the original direction from city council it was to allow sidewalk dining specifically within the village on Monterey Avenue and on the wharf and the draft ordinance also includes allowing it in the mixed-use neighborhoods as well as our commercial, community commercial and regional commercial district. And then the other change in street dining decks. The planning commission recommended limiting the street dining decks to Esplanon and San Jose Avenue and the original direction from city council is to also include Capuchola Avenue and Monterey Avenue. The planning commission recommended removing Capuchola Avenue because of how busy the street is. And then Monterey Avenue because it's a major drop-off location for beachgoers and they thought there was more public good in having five additional spaces for the beachgoers to drop off their stuff for the beach and a convenient space. And then again we talked about the timing of the prototype. So this slide goes over all the items that stay the same between ordinance one and ordinance two. The number of parking spaces for the dining deck was 25. The bicycle parking both ordinances include two bike spaces per automobile space and it also adds an imbue option for a central location for bicycle parking which they can pay it to. Found through the course of the review by the planning commission they recommended contributing amplifiers found in all music. For signs one business identification sign and one with youth sign it both be limited to two four feet each. Materials the planning commission added specific guidance for what material for the appropriate in which materials are not appropriate. The activated space this is the new standard after seeing so many spaces with knowledge for the temporary ordinance not being utilized they added an activation requirement that the restaurants be open a minimum of five days per week and utilizing that space having it available for customers during those days that are open and weather pending. Then storm water we had there's new requirements for storm water and also utilities maintenance we added some specificity of new standards that are related to trash the upkeep of plants and regularly cleaning the areas. Another shift was for the administrative policy originally was the city manager could authorize modifications to the admin policy the planning commission recommended that City House will authorize any modifications to the admin policy so that's a little more nonsense and then private property originally had a section requiring that any private property that wanted to add outdoor dining could through a commissionally used permit. This one I the planning commission recommended we're moving and I want to strongly recommend that we stay with that if we want to include private property we should come back with a really well thought out ordinance on that and how and creating standards for review if we want to include private property I think more time needs to be spent on that. So clue still commission review so that that's that the overview of what was included in the ordinance so we did we took the draft ordinance to coastal commission they reviewed the ordinance and provided feedback they asked us to reduce the amount of permits to about half of what was originally allocated with the COVID-19 permit they also suggested be a temporary program so it can be reviewed and make sure it's working and not limiting any coastal access or creating coastal access issues and then they suggested that we used the funds for outdoor dining for the leases could be should be reinvested into coastal access so all three items have been included in our updated ordinance. Another item is the new concept of a blanket coastal development permit which for the prototype design once the planning commission once we have a prototype design. Okay looks like T.B. is frozen. Jamie Edwards is just a little shaky. I don't remember if we're trying to turn off his camera because he's also having some issues so just want to know that he's still with us and also Jamie just so you know I'm seeing people pass along host permits of access and I just want to make sure that at least someone here in the term still has a host access. I don't know who has host access right now. I've only had to cheat and dally. Interesting. I do see it under my name like how would you like me to proceed? I think you're okay for now just sit tight. Okay.  Can you tell us how many of our slides she has you know when you can? Give me one moment. So the last slide we covered was blanket prototype. You know it looks like unfortunately the version that I'm accessing no that's right blanket prototype. So that's slide eight and we've got through slide 20 so this is better. Okay. Do you want to shoot her a test to see if she's going to be looking at? Sure. And then what we can do is we can move on to the other item items 9c while we wait I prefer her to present just because for sure she's done so much work on this. The um so the internet is down sounds like a city hall. Oh okay. So are we still broadcasting? As far as I can tell we are from from what I can see this is Chloe. Okay I'm gonna um if we have people in the participants I'm going to just do a quick test. Raise your hands if you can hear me. I'll be back. Perfect okay so we're so they are still able to be here so I just get our hands going up and down so it makes me believe we are live still. So it sounds like a city hall there's not a bandwidth at this point to do both video and audio so I just suggested that Katie try to pull you are on for for you are on right now with audio only. Is that correct? I think what I just heard is that you said on television they can hear us and not see us. Is that correct? That is true if that's what you said. And we can hear you just fine I think. Okay. Okay. So Mayor Brooks I think we have a couple options as you just thought why we're gonna have to go to the next item um and continue this for a moment or two uh we would need to get um tell the woman Kaiser back on I think for item 90 you know. Yes and are you whose item was item 90? That's Larry. Larry are you on? Larry's not on. Okay. So we have a problem. Your problem well. Our bandwidth has gone down it's not even metaphorically speaking this is just too close to Thanksgiving. Um I see Katie coming back connecting Larry's back at hosting's are happening um let's take a I'm just going to give it a five minute break right now to see if we can figure this out. So I'm going to officially pause this meeting and take a five minute break Chloe are you with me for that? Yes I think that's a great idea thank you Mayor Brooks. Okay. All right so folks um in the audience go ahead and lower your hands if you can still hear me and we will try to get this back on in five minutes we'll return at 9.44. 7.44. Sorry Campbell. On zoom for a while and it's weird so I don't know why it's working here. Is it working? It was I was on zoom fine. I'm trying to even get to the device and I can't. Oh I mean if you want if Katie wants to come down here it doesn't mean it will work down here. Okay so um because we're all this is all connected to the same equipment. Got it no I understand what can I do anything you ended the zoom. Because I was on my phone. Do you want me to try to open the zoom up again as you? Um I can log in as you. It looks like your name. Everything's working for me. Okay don't do anything now.  Okay I'm wondering why can we still hear what? Is there coming over to zoom? Yeah I don't know what's happening. Well you're the one who said this is the item you thought might be long. Well that's true. I know poor Larry. Is Larry out there? No I won't say anything. Everyone who wanted to speak will be able to and I see a chat from someone saying they can't hear that we're breaking up so that even speaks more to the concern that I have about moving forward with this item tonight not knowing. Just kind of finish that thought there. So for those who are listening or able to listen where we're at now we're concerned that there were some people who might have dropped off this call who really wanted to participate in this conversation. Um and so that we those who are back and here and willing to share your comments we will you will have that opportunity. The the issue is again we're just worrying that some people didn't come back. Um and that there's just some sort of weird internet thing going on out there in the ether world. So I see two council members hands raised. I do want to ask one question though for discussion's sake. In terms of timelines Jamie and how we have our we have a December meeting and the January meeting while we're taking council comments um on this matter can you get timeline and whether we would need to hold a special meeting or when we need to make to keep these things moving along. So I'm going to have I saw like there's stories can't raise and then I'll go to council member for sure. But that's my question Jamie for you to condor. Do you want to answer? No I'm going to take like their story open questions or can first like that story with us. I believe I am. There you are. Yes. Yeah and just to throw another curve into the scheduling mix. We also want to acknowledge that you know one of our tasks this evening was to make a decision on the continuation amount of the temporary parkway program which is fired on January 3rd. So it seems to me that if we're going to continue this we should do it prior to that date or date certain. So we can deal with that issue before the expiration. Okay and we have one more meeting before that on December 9th. Okay council member for if you're trying to talk to other you're on here. So you can hear me that's good. So you know for me as in the chat it's very choppy and I really don't think that I can be a part of a conversation about this. Okay and just a reminder for council members. From all intents and purposes What? I don't know if you hear me. We can hear you. I'm just going to remind our council members who did not read the chat so they're not public comments. Oh it came up as a message. Okay so I got that. Y'all this discussion I think should be in a form which is internet perfect. It's very important. So many people are interested so I prefer to put it off like Kristen said and in recognition of the fact that people are online may not be online in December I certainly think it's important we take the comments now to be part of the record. Thank you. Thank you council member for trans. Council member Jamie are you chomping out a bit? Okay council member Peterson. Yeah I just think based on the concerns that we're varying from from other council members that we should continue this either to our December 9th meeting which I know is kind of a tough one that's when we do kind of our changes in guard and all that. So either that we continue it toward December 9th meeting or we have a discussion about holding a special meeting in the week of December 13th to take only this item. So my recommendation would be at this point that we schedule a special meeting next week which would then let us hold our second and Samantha you can help me out here. I believe as long as you hold your second meeting at least seven days after the first meeting. Okay so we could do a special meeting next week and I think given the challenges that I'm hearing from a lot of folks it might make sense to just do a do-over of the general government agenda this evening. So just because I'm not clear whether or not council member Bertrand you're suggesting that you use every difficult time participating and it may be the best thing to do is to just do a do-over of the whole thing whether or not you want to take public comment the awkward piece about taking public comment this evening is is like I sort of feel like they should see the whole presentation but I think that's your decision.  Are we looking at the date of November 29th then? Is that five days? So on November yes it doesn't need to be five days from today it needs to be five days prior to the December 9th meeting. So we can do we can we can try to pick a date for next week right now if everybody has their calendars available. For those who are here that might be helpful to them too in the audience to know what most of the meetings um the 29th 30th first all uh the 29th 30th and first and the meetings work well with me. Should we do six o'clock on the first? It's Wednesday. Thumbs up from council member Bertrand, thumbs up from council member Peterson. Like my story I don't see thumbs from you um because your camera's not up. Now there's my thumb. All right there's a thumb. So we need to ask council member Kaiser if she's off. We will check in with her but I think we're going to have a yeah we have a um what's it called? Quorum. Checking all of us. Oh quorum thank you. Okay we have that quorum that's required. Okay so council member Peterson would you like to that motion you started? Sure. I'd like to move that we um continue all of the items on our general government for tonight's agenda to a special meeting to take place on Wednesday December 1st at 6pm. And just for clarity that's item 9a BNC. Yes all of all of the general government items from this evening. Jamie is that what you were meaning? Um that was what I was suggesting. Okay. With checking with council member Kaiser to see if she says she's not available on Wednesday. Can we um can I make that a friendly amendment to your motion and that we come back with just item 9a on this first and then continue our meeting for 9 BNC tonight? Yeah. Yeah but I'm having problems participating anyway but okay what is um for the sake of us being able to determine an appropriate time and knowing that by law we need to notify the public within what 24 hours of the special meeting correct so would it make more sense for me to um rebook my original motion and make a new motion that we bring all of the items on tonight's general government and public hearing. Item 9a BNC back to council at uh a date next week uh to be determined and posted by the end of this week. If I motion that that would allow the opportunity for staff to determine with council members over the next three days and we'll wait until Thanksgiving. I can say we don't need to formulate this into a motion. I appreciate the effort I like but you don't need to formulate this into a motion. The council simply decides I may or could decide um consistent with what council appears in our hearing is that the entire agenda will be continued to a date next week. I will listen to part people with households to determine availability for next week um and we will notice the meeting consistent with the brown act requirements which is 24 hours in advance and I will refer to the city manager or maybe the court that she lost about this before and we'll give the agenda out state out sooner and the brown act requires we can do that. Absolutely. I think we're all in agreement. I'm sorry without you. I just was agreeing. I we can certainly do that. I will be in touch to get availability tomorrow and we can have the agenda out and notify the public more than the brown act requires before the end of day tomorrow so that everyone knows when the meeting is next week. Does that sound okay? That sounds great. Okay. So I appreciate all the council's work and staff. Tonight's work on everything um so tonight we are going to go ahead and close our meeting um and come and we will come back at a later time next week um for those who are in our audience just let you know just keep an eye out on the left side and we will try to notify people as well as best as we can. All right so we'll go ahead and adjourn at 8 11 p.m. Thank you everyone for your patience um and for again for those who are here um please share with your those what happened tonight. Thanks again. Let's break your mirror.


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