Well, good morning everybody. How are you doing this week? Good. Yeah, good. I want
to thank Charles real quick. So Charles sent me the pipeline workbook he put together last
night with waste by licensee by day, which is the exact data point that better carbon
solutions has been searching for. And I think it'll be helpful for anyone who's interested
in cannabis waste. And I think we were talking about last week, how perhaps people in other
states could even look at Washington and learn from us. So it could be helpful to waste producers
in other states as well. Good work there, Charles. Do you have any comments? So, you
know, definitely take a look at it and see how you want to display that. There's over
like 1100 different producers that produced waste. And so I wasn't sure how to like display
that. Well, I would just start with just just average. So just what's the average amount
of waste for licensee per day. And so I would maybe start there. So that that way you can
just get a singular time series. You could look at like the top 10, you know, see what
the top 10 producers how much waste they're producing. And then the this one, you didn't
really need to sort it by licensee. But it would also just be interesting to look at
total waste by day. So those are the three metrics that I even start by plotting. And
then those are just all time series. So you could get more creative than that, I'm sure.
Okay. I'll keep plugging away at it. Speaking of getting creative, if you were so one of
my favorite types of figures is a map. So you could potentially have a map and have
almost I'm not sure you could do it. I wonder if you could almost do like, like radial sizes.
So you just have bigger circles around the bigger bigger waste producers. But I'm not
sure if that would really make sense. That way you could almost get like a geographical
sense of where all the waste is. Yeah, maybe like a heat map of the region.
Exactly. A heat map would be a good one. If you're feeling ambitious, but you have the
data so it's possible. And if you need to geocode the addresses, canlytics has a little
utility helper that for the most part, you can just toss in someone's name, just their
licensee name, and it can get you the geo coded address. But anywho, good work there.
And I think how about maybe I'll take a look at it. We can digest it. And then if you want
it, we could look at that to look at your waste data next week.
Okay.
So that's awesome. Good to see you here too, Nick. I've actually got something interesting
for today. So last week we started looking at the inflation of cannabis prices in Oregon.
So finished compiling that data. So let me just go ahead and push it up to the Canvas
Data Science repository here. And then I will go ahead and start showing you just a bit
of the progress that I made on calculating inflation rates since last week. Don't worry,
Charles, didn't miss anything. I'm just opening up the presentation.
All right. So it is actually April 21st. However, this is essentially the presentation I put
together last week. So Charles has seen this. Nick, I'm just going to run through this real
quick just so that you can get up to speed with the data we're looking at and the stats
we're trying to calculate here. So we're looking at total cannabis sales in Oregon, and we
can break that down by product type. And this is available through Oregon's public data
dashboard for the cannabis industry. And they update their data quickly. So you have everything
through the prior month. So you have everything up through March of 2021, which is phenomenal
that they publish their data so quickly. Yeah. Did they fix that dashboard? Because last
time I checked, it was like, I always had issues, but maybe I had this wrong link. But
like it would always just air out and nothing would show up. So that's...
Yes. So the dashboard is a little tricky. So when you access it, it's actually an HTTP
address and you may have to hit the advance, proceed to an unsafe website button. Okay.
All right. And so it may look like the website's unavailable at first, but essentially I think
they're just running on HTTP. And then most modern browsers warn you against navigating
to some HTTPs. It's worse than that. It means that they're
running HTTPs, but they have a self-signed cert or one that can't be verified.
Oh, thanks for chiming in. So exactly. So it's good to hear from someone who knows their
computer science. So thank you for chiming in. So maybe it preceded your own caution,
but in this case I went ahead and collected the data because the data is valuable in our
case. Yeah.
So the other thing is you may figure out a niftier way. I essentially... So here I'll
show you what the data portal looks like. So as we've noticed, it's not secure, but
we're not posting any sensitive data here. So long story short, you can download some
series, but some series it's harder to download. So I actually just transcribed the figures.
And I put them here in the... Well, I may have saved... Oh, yes. So essentially I've
just transcribed the numbers here into Excel. So you can double check my work that I transcribed
everything correctly. But if you can think of a niftier way to collect this data, then
by all means I've added. So back to the presentation. We've collected sales data, we've collected
price data, and we're curious about inflation of prices. So of course you notice right off
the bat that prices in the first two years decreased dramatically as you would expect
in a new market. So as things are coming to equilibrium, as people are figuring out how
to price their products. And then you notice in the longer run, prices seem to stabilize
and you have steady... It appears that you may have steady or inflation at this point.
And so that would just be a moderate increase in prices over time. And then we've noted
that economic theory would suggest that sales and prices would be correlated with the interest
rate. We notice the cannabis industry is a little unique, so it may not be as strongly
correlated with the interest rate as other industries may be. And so we're just going
to see if the interest rate can be helpful to predict inflation in the cannabis industry
or not. So here you see the interest rate is lower to near zero in early of 2020, March
of 2020 to be specific. Well, maybe February. Next, we're going to try to estimate output,
inflation and the interest rate. And just for a reasonable timeline, we'll just do through
2022. So just for the remainder of the year to see what output and inflation may be in
Oregon this year. So we are using economic theory. However, the statistical model is
a theoretical. So it's really just taking any vector of time series and using historical
observations to forecast that data forward. It's useful because you can apply it to really
any time series data. And as long as it's coming in at a regular interval. So you can
look at daily data. If you're looking at actual individual transactions during the day, there's
statistical problems there. And then the other major pitfall of vector autoregressions is,
of course, if you look at our equations from earlier, you'll see that there are a lot of
variables. So sales depend on sales from however many periods ago. So they may depend on one,
two, six periods ago. Likewise, it also depends on inflation from one to six periods ago,
ten periods ago. However, many lags are in the model, similar for inflation. So if you
only have, so in this case, we're looking at monthly price data. So we're just looking
at the number of months really between 2017 and early of 2021. So we have about 50 observations
here. And so we're going to be chewing through our degrees of freedom quite quickly. Because
if, say, you just had one lag, then in this first model, you would have four parameters
times three models. So that would be 12 parameters with just one lag order. So with 50 observations,
you probably couldn't estimate the VAR with a lag order of six. You probably would not
have enough observations. So keep that in mind when you're estimating your VARs. So
that's why monthly data can be a little tricky. Weekly data is nice. And then daily data has
its advantages because you get a lot of observations. And my only observation is it depends on your
forecasting horizon of which frequency you want to choose. This is just a slight note
that just says that we don't actually think that... Well, essentially, we're just saying
we think that people essentially take the interest rate into their calculations when
they're making decisions. So the Federal Reserve can change it however they want. People are
just going to change prices and act accordingly. So enough of that. Let's go ahead and get
into forecasting. So real quick, just to run over the 10. So these are essentially... Professor
Iqbal, UNC Charlotte just told me these 10 steps to forecasting. And they've proved pretty
useful as 10 steps to follow when you're doing forecasting. But first, we need to know what
we're forecasting, the inflation rate in Oregon, cannabis prices. We need to understand why
we're forecasting because we want to know how prices are going to be moving in Oregon.
As we've saw, it looks like there's going to be moderate inflation, but we want to actually
quantify that. And we need to acknowledge the cost of the forecast error. So that would
just mean we can't take too much stock into our forecast. So what's the cost of over
estimating inflation and what's the cost of underestimating inflation? Because if we put
these forecasts out there and businesses make decisions about them based on what they think
prices may be, they need to take into consideration what's the chance of overestimation slash
underestimation. That's a tricky one, but important to take into consideration.
Next, we just need to know the horizon, which we're just going to be forecasting through
2022. Long enough to be useful, however, not too long that it's unrealistic. We've chosen
our variables, inflation, output, and the interest rate. And we've chosen them because
of economic theory. We've then chosen a vector autoregression to do the statistics. And the
vector autoregression is a vector that fits our economic theory and it's a relatively
simple statistical model that we can also use for forecasting.
We'll want to present the results in some sort of figure. I'm not sure if you've heard
of Edward Tuft, but he would always emphasize that you need to show the data in some sort
of beautiful figure. So we'll throw out some charts. Then we'll want to interpret what
predictions we've made. So we'll want to see if inflation is rising and decreasing, what's
our confidence. Next, we will essentially want to use recursive
methods. So what this means is we want to keep forecasting each month. So we'll want
to revisit our forecasts next month and compare our forecasts to the actuals and see if we
can't make better forecasts the next month. And when we do that, that brings us to the
final step is when we look at our forecasts next month, so we'll be forecasting April
sales today. And then in May, we can actually check our
forecasts and see if the model we selected was a good predictor. And we may need to select
a different model if our forecasts were wildly wrong or slightly adapt the model or slightly
stick with it if we can't find a better model. So that's a quick background. Are there any
questions before we jump into the code here? All right. So I just opened up Spyder here.
There's nothing special about Spyder. I'm getting used to running Python in VS code.
However, I'm still just faster and more effective in Spyder. So until I'm better in VS code,
we'll be here. Or another editor. I've heard of PyCharm and Atom. Both seem like good choices.
I haven't tried either. You're a weapon of choice.
The packages we're using today are essentially NumPy and Pandas. And then I'll be reading
in a Federal Thread API key from an environmental variable, an environment variable. And then
we'll be using the Thread API to get the interest rate.
First things first, we need to read in the data. So I'll just be reading in this data
that I've scrounged from the Oregon dashboard. Just to look at the first observations.
Notice, we don't really have consistent prices across the board until about 2017. So a professor
in college really emphasized you never want to throw away data. However, in this case,
just for simplicity's sake, I'm restricting the time period to 2017 and onwards. And that's
for simplicity's sake. If you have a more elegant way to estimate the models, then by
all means.
We're looking at data from 2017 through March of 2021. And first we'll look at the data.
So what are some of the variables we have here? We've got total sales. We've got flower
sales. We've got concentrate sales. I thought we had retail flour prices. Okay, so we've
got retail flour prices. And we have retail concentrate prices. Those are our variables
of interest.
And so last week, we talked briefly about how to calculate the CPI. And just to show
you a presentation from last week with my chicken scratch. We basically noted that the
consumer's basket of goods is about 60% flour and about 40% concentrate. So if you were
going to calculate the CPI, which is a price index. So you can think about a stock index,
which just sort of measures the general trend of all the goods in the basket. And so our
basket is concentrates in flour. So our CPI will be the sum of all prices times their
weight. So we can actually do that here in Python or your favorite programming language.
So essentially, we can calculate the share of flour out of total sales.
Rule number one, look at the data. So as you see, flour was making about 70% of all sales.
And it's actually decreased, which is interesting, and is now maybe kind of stabilizing around
55 or so percent. That's quite interesting. Then you can calculate concentrate share of
the sales. And so this should be roughly the inverse where you have concentrates. Because
keep in mind, there are also edibles and other goods. So we are excluding some data here.
So it would be nice to have prices on edibles, but edibles vary so much across the board
that it may not be key of pairing apples to apples, or infused apple to infused apple.
Yeah, that's what sucks about this is they kind of lumped everything together, like edibles
and like some of that. Because the concentrate would include oil then too, right?
Yes.
So it's a lot of different things that they put in there. And then on the manufacturing
side, we have to consider this as pie retail sales. So if we're forecasting, we have to
actually consider there's a markup. Because we're trying to get back to what we sell it
at. So it gets a little complicated.
That's a really good observation from someone who's in the industry. Because as you noted,
you're sort of aggregating everything here. As you said, there's a wide array of concentrate
goods. So you've got everything. So you've got your really high percentage distillates
to maybe just some lower percentage waxes and other oils. And then, I mean, there are
concentrates being made and sold to edible producers. And then how do you classify something
like just like infused, like, you know, when they make like moon rocks or something like
that.
Because you know, moon rocks would probably be technically a concentrate. But that's hardly
the same thing as oil. But taking it as it, taking what we have, taking our lemons, we'll
try to make lemonade. We've got the sheer flour and the sheer concentrate, which are
actually by themselves are interesting figures. So now we can calculate the cannabis CPI, which
will be the flour share of prices times the retail flour price plus the concentrate share
of sales times the retail concentrate price. And so this, you can maybe calculate this
in a more glamorous way. But that is essentially this calculation here. So we'll calculate
the CPI. Let's just look at it just to see what it may look like. However, the nominal
value doesn't matter too much. It's more about the trend. So the trend, we can calculate
what we've defined as inflation, which is just the rate of change of the CPI. We'll
basically just be looking at the CPI. And so just to go back to the presentation real
quick. So it's basically the CPI of today minus the CPI of yesterday divided by the
CPI of yesterday. Last week, I just made a crude hypothesis that it would be between
1 to 3% on average, which would maybe be what you'd expect then maybe in a more mature
industry. So let's go ahead and calculate this and run it. And I'm actually, so it's
kind of jumping across the board. The first thing I notice is it is negative, which negative
inflation is deflation. Deflation from the federal level is not desirable. So deflation
can have bad effects. So from a consumer's point of view, if there is deflation, then
you know that prices are going to be decreasing. So you know prices next week are going to
be lower than prices today. So it gives you the incentive to wait to buy your goods next
week. So if everybody's just waiting as long as they can to buy their goods, then it's
going to decrease the economic demand even more. So businesses, they're already struggling
for demand. And now consumers are postponing their purchases. So the businesses don't have
much cash flow. So it's even harder for the businesses. They lower their prices even further.
And so you can get trapped in essentially the cyclical deflation. That's sort of bad
in the economy as a whole. Like I said, the cannabis industry, it's sort of this short
term shock where things needed to stabilize. There was just this, I think there was even
a note in Oregon where they're just saying, oh, there's so much supply. So just in this
first few years, everyone's sort of figuring out, okay, what is the level of supply? What's
the level of demand? And prices, it appears, were just too high. Or not too high, but maybe
those were the prices maybe when you were expecting more of a gray slash transitioning
from a black market. And then as more producers entered the market, you see things stabilize
over time. So it looks like it took about two years for people who were thinking about
entering the market to enter. So this is the time essentially where everybody's entering
the market and the people who aren't successful maybe exiting the market. And then it looks
like by about 2019, things are starting to stabilize. So let's actually just look at
inflation in percentages real quick, because that's typically what you think about. Then
let's just look at the mean. So as a whole, prices are decreasing. I would just note that
if you just look at the last two years, if you just look at the last two years, inflation
maybe is around 0.5% per month, which is maybe that's closer to typical. So maybe moving
forward, you may expect more, this slight 0.5% a month inflation. Maybe there's some
shocks here and there, but personally, I wouldn't expect this deflation to continue. So that's
just sort of our crude data analysis. So now let's grab the interest rate and start forecasting
here. So you can get your API key for free from the St. Louis Fred. They're a good resource.
Just starting to try to incorporate them into my work just to get some more data points
in here. This is the effective federal funds rate. It can be a good starting point. And
it looks like something went wrong here. Okay. So not 100% sure. I'll have to investigate
what that was after this. But long story short, we've got the interest rate now. I think that
was maybe some sort of error maybe reading in my API key. Anyways, moving on. We've got
our interest rate now. So we've basically just read in the tail end here of the interest
rate. And we'll use it in our vector autoregression. And like I said, next month we can compare
our forecasts and perhaps even maybe even next week we can maybe even try a different
forecasting model and then compare the two forecasting models. So we could try next week
to just do an autoregression of inflation. And then we can compare that to the vector
autoregression and see what forecasts better, the more complex model or the more simple
model. And that takes us to our 10 commandments where we wanted to use recursive methods.
Okay. So we have our total sales, we have inflation, and we have the interest rate.
We're going to toss all of these together into a vector. And so just to show you what
the vector looks like. So basically this is sales, this is inflation, and this is the
interest rate in time period zero, which would be 2017 January. So this is sort of what the
vector looks like. So just to show you what's happening under the hood. And so now I wrote
a scrappy vector autoregression and I wasn't actually very pleased with how it was working.
So I actually noticed that you can actually just use a vector autoregression in the stats
models package. So it saved a lot of time. And it's a bit more flexible. You've got more
contributors, so more stable than my scrappy var model. So we'll use this one from this
package. And so let's say you're just going to fit. Well, this one has my notes on it.
So here's our three equations. So let's go ahead and fit this. So output inflation, the
interest rate. And we're just going to use one lag order. And so what we've done here
is we've basically estimated three ordinary least squares regressions simultaneously.
So this is our first regression. This is our regression for output. Then this is our regression
for inflation. And then this is our regression for the interest rate. And so as you see,
each one has a constant. And then these are the coefficients. So this is beta, this is
gamma, and this is delta. So we've estimated quite a lot of parameters here. I'm not sure
if they tell us our degrees of freedom here. But long story short, we were able to fit
an AR1. So that's a good sign. Next. If you were going to fit multiple models, so say
we're going to fit an AR2. So we're now going to use two lags. So here are our three simultaneous
equations. And now you'll notice that we have two lags of each. Yes, so two lags of each
variable. So there's output lagged once, output lagged twice. Inflation lagged once, inflation
lagged twice, interest rate lagged once, interest rate lagged twice in all three models. So
that is a lot of parameters. That is 21 parameters, I believe. So that's one, right? So that's
six, seven times three should be 21 parameters. So I'm not sure if we can, it's going to be
tough to estimate too much larger. However, we can try. But we need some way to compare
these models to each other, right? Because we can just do this all day if we have a large
enough data series. So centrally, we need to have a criterion. Centrally, the BIC, the
Bayesian Information Criterion, it rewards you for making better predictions, but then
it punishes you for adding more and more predictors, more and more parameters. So the BIC is a
useful way to measure which model is a better, which model may be a better choice. So as
you'll notice, this model, we tossed in a ton of parameters, so we may be able to predict
a little better. But we've got a BIC of 22, and this model has a BIC of 21. And so we
want to pick the model with the minimum BIC. And so in this situation, the AR1, it's more
parsimonious and it's actually the better model choice if you're choosing by BIC, because
you're essentially overfitting with this model. And it may lead to bad out of sample predictions.
So that's sort of a long explanation of what we're about to let stats models do for us
automatically. So we can actually just let stats models say, OK, why don't you try every
model up to lag order six and just tell me the best one based on minimum BIC. So we can
just let stats models do that for us and save us a lot of programming, because you could
program that up yourself. But you let stats models do it. And look at that. They tried
every model that they could fit up to lag order six. I'm sure the higher order lags,
they weren't able to fit successfully. And so they determined, OK, the model with the
minimum BIC is actually the lag order one. So that's just our first model.
What this is is this model, but without the dot, dot, dots. So we're literally just doing
YT on YT minus one, inflation of T minus one, the interest rate of T minus one, and so forth.
So we finally get to the goods. So we've got our best model. It's forecasted and show the
data. Maybe I ran that twice. Not certain why it's printing twice. But anywho, here
are our forecasts. So this legend's a little in the way, but there's our forecast for our
total sales. Here is our forecast for inflation. So slightly rising. And then our, see, this
is interesting. So our forecast for the interest rate is negative. But as we know, the interest
rate can't be, well, it can't be nominally below zero. So the Federal Reserve has to
use monetary policy, quantitative easing, and other tools in their toolbox to effectively
drive the interest rates below zero, if that's with their policy. So that's the plot. I will
admit that I am just now learning the stats models packages, because I would like to actually
print out with this series is here. But I am not certain that I know the thing. I may
need to learn the stats models package a little bit better to actually get you these raw data
points here. However, here is the package I'm using. And there is a lot here. So I've
just hit the surface. You can do error analysis to make sure you're not doing anything crazy.
We've done the lag order selection. And then I think for maybe in the future, I would like
to show you how to do impulse response function. Because you can actually say, okay, well,
what would actually happen if there was a shock in prices? How would that affect output?
Or if there was a shock in the interest rate, how would that affect prices or sales? So
all three of these series are theoretically related to each other, particularly sales
and prices. So long story short is you can use these impulse response functions to simulate
shocks. So what would happen if all of a sudden there was a recession? So these could be useful
for simulating harvest spikes in October or something like that. So perhaps for next week
we can dive into that. And I'll actually want to show you the actual numbers here. But that
is sort of the conclusion of my spiel here on forecasting inflation. And so I just maybe
like to open it up if there are any questions or anything.
Would be able to, is that beneficial or there are avenues for better analysis next week?
Or?
No, it was interesting. These are things that I don't think about. So it was good to learn
about how you go about forecasting these things. So it was cool. I learned something.
What are some of the things you think about? Because this may have been, I'm not sure,
this may have been a bit too economics heavy. So what are some of the things that you're
thinking about on a day to day basis?
Well, that's kind of in my data science journey. I realized there's like a data science food
pyramid of things that you should know. And so I know everything at the top and I know
everything at the bottom, but there's this middle business understanding, business storytelling
kind of thing, which I don't know. And so I'm trying to learn that kind of thing. And
these presentations have been really good for that.
Well, that could also just be my style. So that's sort of, from my experience, that was,
so I attended a lot of seminars when I was in college. And that's what I noticed made
the most successful presentations slash papers is when you sort of tell a story and you let
the models evolve. So you start with some sort of business question. So here we started,
it helps to start with just a figure. So start with the data. So we just started with what
we're looking at sales in Oregon, prices in Oregon. And we sort of just let the model
evolve naturally. So we start as simple as we can with essentially the growth rate. So
you start with the simplest statistics you can calculate, and then you gradually make
it more complex. So really before you jump to the VAR, the VAR, you'd really want to
just estimate a VAR, which we've kind of done. Well, I'm actually, I'm not sure, I'll have
to review it. We may have done that prior weeks, but essentially you let the model become
gradually more complex, but still keeping in mind your initial objective. And then you
just, yeah, you, and then as you're doing your analysis, you'll be calculating other
metrics of interest. So we calculated the share of flour, we calculated the share of
concentrates. All of those sort of add to the story we're telling here of this presentation.
So you sort of build these pieces here. They're just sort of pieces of your analysis that
just gradually gets, you just keep adding to it until you've, you know, you've done
sort of an in-depth analysis on the subject at hand. And then you've answered sort of
your question at hand, which started by just looking at the data. We just basically wanted
to know what's the trajectory for inflation. What even is inflation? So we were able to,
you know, gradually build up to that. And then we can answer that question. We can say,
oh, it looks like despite, you know, historic deflation in the first two years, you know,
we predict, yeah, moderate inflation. And I wish I could print the numbers and actually
give you the number here. But yeah, we predict, you know, moderate inflation here throughout
the rest of the year. And you could keep taking this story further and keep adding steps to
it. So you could say, oh, like, what's going to happen if there's a harvest spike or what
about this? Or can't we estimate an even better model? So, and that's where sort of papers
build on each other is because then you kind of can continue someone else's story by saying,
oh, picking up this story where they left off, I'm going to, you know, add to their
model. But that's my approach is just start off simple and then just sort of document
your steps. You know, instead of just jumping to the end and just saying, oh, I'm just going
to do a, you know, principal components analysis, or I'm going to do, you know, machine learning
algorithm, XYZ, you know, instead of just jumping all the way there, I find it's more
informative to start with the basics and then build your way up until the machine learning
algorithm is actually like the next reasonable next step. Because eventually, if you kept
taking this analysis further and further and further, I mean, machine learning would be
the rational next step. I'm just not, I just wouldn't start with it right out of the gates.
So that's a long-winded answer, but, you know, I resonate with that statement.
Yeah, cool. That's, yeah, that's, you know, I think kind of the lesson that I've been
learning and everybody emphasizes machine learning a lot, but actually there's a lot
of steps before you get there and before it's even useful. Recruiter. They're like, they
never stop calling. But anyway, I read a really cool article in the Oregonian and I'm trying
to figure out how to share it because the Oregonian is making it more and more difficult
to read their articles online and share them. But there was an article about the counties
and the towns along the Oregon-Idaho border and how, because, you know, cannabis is legal
in Oregon, but not in Idaho, that these towns have, you know, like on the weekend, they
have huge, huge influxes of people from Idaho coming in and like the per capita cannabis
sales in these towns is like four and five times the amount it is in like Multnomah County,
which is like the most populous county in Oregon. Oh gosh. Oh, because people are coming
in from Idaho. Coming in, yes. And so, and, you know, and there was a statement from like
some, somebody in New Mexico and they're like, yeah, we needed to legalize it before Texas
does so that we can, you know, we can capitalize on those sales. So it was really interesting.
I guess that's one of those things where it's so hard to capture that in the data. And I'm
not even sure what the laws on that are. Maybe, but that's up to the individuals who are doing
that, I suppose. But I know there's a town called Ontario, Oregon, that's right on the
Oregon, Idaho border that has a pretty big dispensary. So, well, has a dispensary. So
they're making money from both states. Well, the people in Oregon are just saying, hey,
we're just, we're just opening up shop. We're not doing anything wrong. Yeah. I don't know.
I don't know. That's a, that's an interesting observation. And I, yeah, that's something
to think about Charles, if there's any way to parse that out of the data. I don't know
if that Oregon data has a geographical, like component to it, but I don't know. That may
take a bit more exploration. I'm not sure if you can get sales by licensee in Oregon
or not and try to do a geographical analysis. Well, I mean, whoever wrote, whoever wrote
this article is able to figure it out. They were able to do like a per capita analysis.
Oh, okay. Okay. So you may, they may have county by county data. So you may actually
be able to do some sort of analysis there. So that perhaps actually for a future meetup,
we could do geographical analysis there because like I said, I think the geographical dimension
is an incredibly interesting one. It's can be tricky, but we've got the counties. So
that, I mean, there's no reason why we can't do some sort of analysis. Yeah. Yeah. And
that Ontario is only like Oregon's only like less than an hour away from Boise, Idaho.
So I've met Boise's people from Boise are like, cause Boise is a college town too. So
you, you probably have a lot of people from Boise just drive down to here to pick up stuff.
That would be a real interesting analysis. And you could extend that to a bunch of states
because that would be interesting to see if you have like this like border phenomena in
cannabis states where of course you would expect like the cities to be like high in
sales, but then it would be so funny if like the border towns were all high sales too.
Yeah. That's interesting observation and could be a good opportunity for analysis. So I think
I'll go ahead and just, unless anyone's got any more topics at hand to talk about it,
we've kind of reached the hour. So let's anyone else have to throw on the table? I mean, go
ahead and go ahead and conclude it here until next week. All right. All right. Well, thank
you all for coming. Thanks, Ryan. Thanks Charles. Thanks Nick. Thank you for your contributions.
It's always, it's always awesome to hear from you. And then next week we'll see if we can't
look at Charles's waste data, touch up the inflation data if needed. And then, and then
maybe start some geographical analysis of if we can find that county data. All right,
everyone. Thank you. Thanks. Thanks for coming. And until next week, have an awesome week.
Bye. Bye. Bye.