My name is Keegan. I founded a company, Canlytics. We primarily help out laboratories that test
cannabis. And we'd love to hear about what you do in the space or are interested in in
the space so we can address some of your interests.
Hi, everyone. Like you mentioned, my name is Jorge. You can call me George if you like.
I've actually been in one way or another since the year 2002. I have a bachelor in computer
information system. I'm from New York and I'm also a cannabis user since the age of
16, I would say. And I'm waiting for the permit to come out here and excited to learn as much
as I can. I already incorporated ready to put in a dispensary here in the state and
looking forward to the new adventure that we're going to have here as New Yorkers with
once this is all done and settled. And I just caught up a little bit on the previous, your
previous lecture. I just finished watching that video and it's very interesting how you
use the program that you use on your GitHub repo. And I was trying to follow along and
I cloned the repo. So I'm going to see if I can follow along and teach myself how to
use it a little better. But it's great. From what I've seen so far, I love what you're
doing. Again, hi everyone. I'm excited to be here.
Hi Jorge. Awesome Jorge. I love that you found the repository and the videos. So by all means,
the source code's there for you to use however you can get value out of it. And as you said,
you're up in New York. So we've been looking at data in Massachusetts and today you're
lucky enough that the very least value I can provide to you today is I can share with you
data that I collected from Illinois. So we can finally do an interstate comparison so
we can compare Massachusetts to Illinois. That's so interesting. Let me introduce myself.
I'm also on the same boat as you. I'm very interested in cannabis and data. And I mean,
I love both. And I think, you know, if you're a user, it's very important to inform yourself
and to be part of this time where like so many things are being discovered about cannabis
and based on data, right? So important. So yeah, just wanted to introduce myself. For
a professional, mainly a software developer, but I consider myself a data lover. So I do
a little of everything. So yeah. Oh, and my name is Carolina.
Long story short, we needed some data from other states. And as ambitious as I was that
we're going to get all the states, we will get them eventually. We had to start with
one. So as we were looking in last Wednesday, these are the statistics that we began to
look at. And so as Marjana is brilliant enough to point out.
Hey, I didn't introduce myself with it that like we were. Yeah. Anyways, I've been to
like two of Keegan's, Ken Littix data science presentations. I was in academic research
for 10 years. And then during the pandemic, I switched fields, I was done with being in
lab, but I primarily work in software development now. And I also love data just like Carolina.
So that's great. And I love cannabis too. I'm from Michigan.
Wow. Okay, great. Well, yeah. I'm with you. I didn't go into detail because because of
time I didn't want to huddle up all the time. But yeah, my situation just to go a little
deeper. Yeah, I guess that before I've been in the industry since 2003, one way or another,
you know, and unfortunately, my path was not the same. I ended up you know, you know, I
don't like to go into this a lot. But yeah, the social justice thing affected me early
on as soon as I finished college, and I didn't have many opportunities as a new grad. So
I took another path, you know, here in New York, I ended up running my own car service
business for a few years. And I've been in a different fields for many years. But because
of the passion I have for technology, I've always followed along on every single field.
You know, I've built apps and react, I've built those in different languages, Python
and this and for a person like myself, that has been affected, has been, you know, criminalized
for things that now today, you know, marijuana and stuff like that, we see it's not as big
of a deal as we grew up thinking, fearing, you know, consequences and the prosecution
for using it as a young man, a brand, brown Latino man. And now seeing how the world is
changing, I personally never thought I would live to see this day. So I'm very passionate
about, you know, hoping, you know, to be an early retail business owner here in New York.
And also seeing how I can implement my knowledge in web design, and also data analytics, and
what I can learn here, to maybe help Keegan input some data from New York as soon as we
start, as soon as they start offering these licenses. So I'm just waiting, I call every
week the cannabis management office in New York, hoping that, you know, I could get a
license. But yeah, we're still waiting day by day. So hopefully soon I can give you guys
more information. So Keegan, with that, I'll cut off.
That's awesome, Jorge. I caught in there at the tail end of it, and I heard that you have
experience doing react and I love your, you know, your mission and I share your dream
too. So long story short, I think I'd love for you to get value from CanLytics. And like
you said, there may be ways that you can help out too, because for example, the react can
be quite useful for the user interfaces. And we're always trying to think of new innovative
ways to get data to and from places. So that is, that's awesome.
Oh, well, thanks for sharing that. And just to add a little something, actually, because
right now I'm really digging deep into the blockchain and building DApps and Solidity.
And just from catching what I caught on your previous week, you know, I just saw the, finished
watching the full video of last, I think it was Wednesday's lecture. I can really see
it being useful to have this data somehow on the blockchain and then having some sort
of live API or DApp where you could constantly see every state as they start legalizing and,
you know, giving out more public data. I could really see it being useful to make it a public
record. And I think that's where the blockchain might be useful. So I didn't want to add that
in there because maybe that could be of help as well.
That's really cool, Jorge. Thank you for sharing that.
I think you've, you're onto something there, Jorge. And I would say just attending some
of these public meetings, because I think a lot of the commission's looking about ways
to make the data more traceable, more transparent, right? Because we're all concerned about data
authenticity. So, and data availability. So I think your insights could, could, could
add a lot. Because, well, unless anyone else has anything to chime up about, I could start
to try to show you some data as long as the internet's stable. Does anybody else have
any thoughts, comments, ideas? There's Illinois Day here. Unfortunately, it's in PDFs, which
can be a little tough to, to extract from. So long story short, we've, I try to, to work
with a few tools to just try to extract the text. And so that's still a work in progress.
But I was at least able to get the data relatively in a, in a manual process. So with the help
of Python a little bit, and so I can share you the script I wrote to get this data. And
so here is, here are the Illinois licensees that are licensed for retail. And so we were
looking at retail the past few weeks. And so it would be interesting to look at, say,
retail in Massachusetts to Illinois. They're both on this end of the curve, but we can
still, we can still compare the two. One dispensary per a thousand, a hundred thousand people.
Massachusetts 1.5. So in, for Jorge, these are states that are relevant to you because
Massachusetts is right next door. So that could be a good comparison state. And so we're
going to introduce some statistics. So one thing you'll learn is good data in, and you
can get away with a simpler model. So often if you've got real nice data, you don't necessarily
have to have the most elegant model out there. So if you can get it, panel data adds so much
power to your statistics. So there are two awesome models, fixed effects and the random
effects models, where we're essentially adding another dimension. So we've been looking at
data over time. And now we're going to say that data can also vary by this individual
dimension, which we'll call the state. So we'll have Illinois as one state, I1, and
then Massachusetts as the other. And then, you know, I just got some things from Wikipedia
here. So long story short, check out Wikipedia or a better source here. But the long story
short is if you expect the error term to be uncorrelated with your independent variable,
so in our case, the independent variable is retailers per capita, and we think this effect
is coming from the state. So if this effect has, if this is independent
from the effect of the retailers, so it's useful to look at this line. So we know that
the states are different. But if the way they're different also affects the relationship, then
we need a fixed effects model. If the way the states are different doesn't really affect
this relationship, then we're better off using the random effects model. And so to give you
an example of the way I think the error may be correlated, so take for example California.
There's an unmeasured variable there, the black market, where California has a larger
black market than Massachusetts. So that can be captured by either this fixed effect or
a random effect. But will that variable, the degree of the black market, with that effect,
the effect of increasing retailers on sales per retailer? And so that's sort of the argument
you have to make when you're justifying which of these models you're going to use, because
it's essentially an assumption, whether you're assuming that this, what they call latent
variable, which is just some unmeasured variable, whether that's correlated with your error
or not. So one could argue that, oh, if there's a large amount of black market sales, then
that could affect sales per dispensary in some way, either positively or negatively.
So long story short, you could argue that the fixed effects model may be better. But
that's just essentially a story that I'm telling. So it really depends on your use case here.
If you're presenting this to a room full of economists or statisticians, they'll start
to just ask you about these latent variables that they think up of. So you just have to
kind of think about how these factors may or may not be related to this relationship.
So like I said, that's probably a poor explanation of the random effects or the fixed effects
models. But I just wanted to try to introduce them today. And you may just have to pardon
my poor introduction, and I'll try to iron it out for next week. And then we'll just
make up for it with good data. Without further ado, let's look at the Illinois data, and
then we can look at Massachusetts, and then we'll look at them together. So just grabbing
some fairly common Python packages plus a couple of utility scripts I wrote. So this
is the cleaning that mostly was done manually. A critical data point, in my opinion, which
is the issue date. So this is cool, because we can now figure out approximately when the
retailers began to operate. So let's do exactly that. So keep in mind, the two statistics
here, we're trying to get our retailers per 100,000 and sales per dispensary. So a little
out of order here, but just going to go ahead and get Illinois's population from the Federal
Reserve Fed Fred. I may have in that time just read in the population for Illinois.
So we basically now have Illinois's population for 2020 and 2021, and also just manually
parse the Illinois sales data. So once again, need a good automated way to do this, but
at the moment it's in a PDF, so that makes it tricky. But we have Illinois sales, and
so we're interested in a couple things here, right? Sales per retailer and retailers per
capita. So what we can do is, well, let's find out how many retailers there were at
each point in time. So here's sales over time. Well, we know when each of these licensees
opened. So similar to how we approach things in Massachusetts, we'll say the retailers
began operating when they got their license issued. So that way we can try to get a measure
of when retailers began to operate over time. So this is an interesting statistic, right?
There are the first, like I said, there may have been some entrepreneurial people out
there that have already calculated these statistics, but I'm not certain that they have. So maybe
we should be humble. So maybe there are people out there who have calculated these statistics,
but we can do them in an open source manner. And so now we can actually get a count of
these retailers over time, which is helpful information. Because just because there's
110 today, we can see that they were kind of, it took a year and a half for Illinois to
get to 110. So we can now get a nice calculation of sales per retailer. So let's just calculate
both of these real quick. Remember, we're comparing these statistics to statistics that
were calculated by Nevada's technical memorandum that was commissioned. So we're looking for
one dispensary per 100,000 and 10.8 million in annual revenue per dispensary. So we can
start to calculate some of these statistics here. So just to grab some of this, it's already
been done. We can calculate the average 2020 sales. So that's going to be the total sales
here in Illinois. That's interesting. So we calculated in 2020, there was only 670 million
in sales per retailer. I'm going to have to think more about this statistic. That doesn't
seem right though, for some reason. The main thing of value today is the actual data, which
I'll email you here after the presentation. But we can at least power through this and
try to get some of these other statistics here. So the other statistic we were curious
about was retailers per capita. Here we've plotted retailers per capita. And keep in
mind, we're using the entire population of Illinois, whereas the technical memorandum
used the adult population, which is a better metric. I just haven't found a source for
this data yet. So once we find a good source for the adult population, we can compare Apple.
Well, really, until 2021, there was less than a half of a dispensary per 100,000 to one
dispensary per 200,000. So we've got this statistic, and we can continue to monitor
the statistic over time. So this is cool, because we now have these two variables, and
we can now redo this regression in Illinois. So that way we can basically zoom in on Illinois
and not only that, but also add a whole other dimension. So we'll be adding the time dimension.
So that's essentially what I mean by zoom in on Illinois.